M.Sc. Visual Computing (from winter 2026/27)

• Module Handbook
• 👋 A warm welcome
• 🧭 Orientation
• About the study program
• Orientation regarding the standard study plan (Regelstudienplan)
• 📰 News
• Module news
• Use of generative AI for theses and exam (preliminary) work
• Student Council of the Faculty of Computer Science (student representation)
• Academic Club
• University Awareness Portal
• ⚠️ Regulations of the Examination Committee
• Modules for Visual Computing
• Ausgewählte Algorithmen der Computergraphik
• Computational Geometry
• Computer Graphics - an Introduction
• Computer-Assisted Surgery
• Flow Visualization
• Introduction to Computer Graphics
• Medical Visualization
• Narrative Visualization
• Three-Dimensional & Advanced Interaction
• Virtual and Augmented Reality I
• Visual Analytics
• Visual Analytics in Health Care
• Visualization
• Wissenschaftliches Individualprojekt
• Modules for Computer Science
• AMS Lab Projects
• Advanced Database Models
• Advanced Programming in Computational Intelligence in Games
• Advanced Topics in Databases
• Advanced Topics in Deep Learning (Master)
• Advanced Topics in Networking
• Applied Discrete Modelling
• Clean Code Development
• Data Mining I - Introduction to Data Mining
• Data Mining II - Advanced Topics in Data Mining
• Data Science with Python
• Datenbanken 2
• Distributed Data Management
• Estimation for Autonomous Mobile Robots
• Evolutionary Multi-Objective Optimization
• Filmseminar Informatik und Ethik
• Grundlagen der Theoretischen Informatik III
• Human-Centred Manufacturing and Usability
• IT-Security of Cyber-Physical Systems
• In-Memory and Cloud-Technologies
• Introduction to Deep Learning
• Introduction to Distributed Sensor Data Fusion
• Introduction to Machine Learning Safety
• Learning Generative Models
• Logik 2
• MLOps for Small Language Model Applications
• Machine Learning
• Management of Global Large IT-Systems in International Companies
• Multimedia and Security
• Musik Information Retrieval
• Neuro-Symbolic Integration
• Praktikum IT Sicherheit
• Recent Topics in Business Informatics
• Recommenders
• Scrum-in-Practice
• Selected Chapters of IT Security: Orchestration of Mechanisms and Tools
• Seminar 'Advanced Topics of KMD'
• Seminar on Advanced Topics of Predictive Maintenance
• Software Defined Networking
• Software-Development for Industrial Robotics
• Swarm Intelligence
• VLBA - Systemarchitekturen
• VLBA – Cloud DevOps Technologies
• Wissenschaftliches Individualprojekt
• Scientific Teamproject KMD
• Modules for Schlüssel- und Methodenkompetenzen (Soft Skills)
• AMS Lab Projects
• Clean Code Development
• Data Science with Python
• Data and Knowledge Engineering
• Design-Projekt
• Scientific Writing
• Scrum-in-Practice
• Selected Chapters of IT Security: Orchestration of Mechanisms and Tools
• Sensor Networks Seminar
• Soft Skills für Masterstudierende
• Wissenschaftliches Teamprojekt
• Scientific Teamproject KMD
• Master Thesis (Masterarbeit)
• Masters Thesis
• 💬 Feedback
• 👑 Credits

Module Handbook

for the study program Master of Science (M.Sc.) Visual Computing

of the Faculty of Computer Science

valid for SPO 2024 and older SPOs*

valid for winter term 2026/27 and preliminary for the summer term 2027

as of 01.10.2026

* For examination regulations (SPO) older than SPO 2024, different standard study plans apply and must be taken into account accordingly. Please pay particular attention to differing compulsory courses and credit points (CP). Courses that do not match your SPO may not be credited in full or at all. Also check the module allocations in the module handbook for the winter semester 2024/2025. In case of inconsistencies or uncertainties, please contact the Examination Office.

👋 A warm welcome

Dear students,

Welcome to the module handbook for your study program.

The module handbook contains all the modules you need to successfully complete your studies. A module is always a teaching unit consisting of several courses (e.g., lectures and exercises) designed to help you acquire skills and abilities during your studies. All modules are described in detail in the module handbook so that you can plan your studies more precisely.

🗺️ What can I find here?

In your module handbook, you will find:

• An 🧭 orientation chapter that summarizes the degree program, provides guidance based on a sample standard study plan, outlines possible study profiles for your degree program, and lists modules from other faculties. 
  
• The chapter 📰 News informs you about changes to modules compared to the previous semester, provides tips on using AI, information from the student council, and more.
  
• In addition, the ⚠️ regulations of the examination board are linked.
  
• The chapters Modules in the area [...] up to the chapter Master Thesis then list the modules that are compulsory or elective for your degree program according to the standard study plan.
  
• A page with a link to a feedback form to collect suggestions for further improving our module handbooks in the future.
  

⭐ How can I use this?

1. This overview helps you plan for this semester and future semesters. Most modules are not offered every semester. Some modules build on each other. This requires forward planning if you want to study a specialized subject.
   
2. In the elective area, the form of the exam can also provide information about your own capacity for the exam workload.
   
3. To check which modules you have already successfully completed and which ones you still need to successfully complete your degree.

❓And what about ...?

If you have any further questions that you cannot answer yourself using the module handbook or the study and examination regulations, please contact ......

•  your study program director for questions about your studies or the applicability of modules, or
  ...
• our examination office for individual study-related questions.

🏆 The faculty wishes you every success in your studies!

🧭 Orientation

About the study program

Master in Visual Computing

What is the program about? 

The Master in Visual Computing at Otto-von-Guericke-Universität Magdeburg provides you with a world class education in visual computing, with a particular emphasis on visualization, computer graphics and computer vision. With state-of-the-art facilities, a broad and research oriented curriculum and a small number of students the program provides you with an excellent preparation for your a career in visual computing in both industry and academia.

Your career options include: 

• special effects for films
• computer games
• autonomous driving
• medical imaging
• augmented and virtual reality

For whom is the program?

The program is aimed at students who want to pursue a career in visual computing and have prior experience in the area. You will typically already have taken multiple courses in the field and now want to obtain in depth knowledge. You also have to hold a bachelor degree (or equivalent) in computer science, engineering, mathematics, physics or a related discipline. 

How does the program look like?

The program consists of four semesters of in-class teaching and project work. The first semester lays the ground work by ensuring that you have the required breadth and depth for the remainder of the program. For this, you will take foundational courses on areas of visual computing that you haven't been exposed so far. In the second semester you will deepen your knowledge of visual computing by taking more specialized courses, for example on flow visualization, medical imaging, augmented and virtual reality, flow simulation, or object recognition. This is continued in the third semester and complemented by a research oriented project, typically taken in small groups, where you can demonstrate your ability to apply the tools of visual computing creatively to solve open ended problems. In the fourth semester your focus will be on the research for your master thesis.  

For more details and a list of courses offered please see the example study plan . Students can also take courses from other departments, for example on fluid simulation, mathematics, or medical imaging subject to approval by the study program coordinator.

Orientation regarding the standard study plan (Regelstudienplan)

The graphic shows the standard study plan with a start in the winter semester. All modules and module areas are linked. The links in the winter/summer semester may change the module handbook. This serves to provide better orientation and study planning. The standard study plan according to the SPO can be found under study documents.

Example

1. Semester	2. Semester	3. Semester	4. Semester
Visual Computing (33-66 CP)			Masterarbeit (30 CP)
Computer Science (SPO 2027) (18-24 CP)
Schlüssel- und Methodenkompetenzen (Soft Skills) (6-12 CP)

• Visual Computing
• Computer Science
• Schlüssel- und Methodenkompetenzen (SMK)

📰 News

Module news

New modules this term

Modules not offered this term

Module changes

Use of generative AI for theses and exam (preliminary) work

🇩🇪 Der Fakultätsrat (Beschluss 019/25) hat folgende Regelung für die Nutzung von generativer KI für Abschlussarbeiten und Prüfungs(vor)leistungen beschlossen:

Die Verwendung von Inhalten, die durch künstliche Intelligenz (KI) in einer Arbeit oder einer Antwort auf eine Frage generiert wurden (einschließlich, aber nicht beschränkt auf Text, Abbildungen, Bilder und Code), muss offengelegt werden. Das verwendete KI-System ist anzugeben, und bestimmte Abschnitte der Arbeit bzw. Antwort, in denen KI-generierte Inhalte verwendet werden, sind zu kennzeichnen und mit einer kurzen Erklärung zu versehen, auf welcher Ebene das KI-System zur Generierung der Inhalte verwendet wurde. Ebenso soll der Grund für die Verwendung der Tools angeben werden.

Die Verwendung von KI-Systemen für die Bearbeitung und Verbesserung der Grammatik ist gängige Praxis. Auch in diesem Fall ist eine Offenlegung wie oben beschrieben erforderlich, sie kann aber summarisch für die gesamte Arbeit erfolgen.

Auch bei Verwendung von generativer KI muss die wissenschaftliche Eigenleistung selbst erbracht werden.

🇺🇸 The Faculty Council (Resolution 019/25) has adopted the following regulation for the use of generative AI in theses and (preliminary) examination work:

The use of content generated by artificial intelligence (AI) in a paper or answer to a question (including, but not limited to, text, illustrations, images, and code) must be disclosed. The AI system used must be specified, and specific sections of the work or response in which AI-generated content is used must be marked and accompanied by a brief explanation of the level at which the AI system was used to generate the content. The reason for using the tools should also be stated.

The use of AI systems for editing and improving grammar is common practice. In this case, too, disclosure as described above is required, but it can be done summarily for the entire work.

Even when using generative AI, the scientific work itself must be performed independently.

Student Council of the Faculty of Computer Science (student representation)

The Student Council of the Faculty of Computer Science (FaRaFIN for short) is the elected representative body of the students of the Faculty of Computer Science.

It is best known for organizing various events, such as the introductory week, preparatory courses, game nights, Christmas parties, and summer festivals.

In addition, they represent student interests in various committees, such as the Faculty Council, Study Committees, and the Examination Board, with the aim of improving studies and teaching.

Academic Club

The Academic Club is a student-run initiative, led by Internationals at the faculty of computer science. The club will focus on Master's theses and will organize various events and activities related to this topic.

More information on the Academic Club.

University Awareness Portal

In 2025, the university introduced a new awareness portal. Awareness here refers to topics related to campus life that highlight social inequality, mental health, gender roles, discrimination, and much more.

In doing so, it is fulfilling its responsibility to provide a safe environment for students, teachers, guests, and other members. The portal therefore provides information and sees itself as a mediator for all topics related to awareness:

Discrimination, disadvantage, (sexual) harassment, and violence
Scientific integrity
Feedback on studies and teaching

and more

Advice and complaints can therefore be found on the new Awareness Portal.

⚠️ Regulations of the Examination Committee

This collection of regulations summarizes relevant resolutions of the Examination Committee of the Faculty of Computer Science. In case of doubt, the wording of the resolutions of the Examination Committee applies.

They can be found here.

Modules for Visual Computing

Ausgewählte Algorithmen der Computergraphik

Winter

(engl. Selected Algorithms in Computer Graphics )

Modulnummer: FIN-INF-120272

Kürzel AACG

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Christian Rössl
Dozent:in:	Christian Rössl
Lehrveranstaltungen:	Vorlesung Ausgewählte Algorithmen der Computergraphik Übung Ausgewählte Algorithmen der Computergraphik
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. VC: Visual Computing

Angestrebte Lernergebnisse:
Die Studierenden

• verstehen grundlegende Konzepte der Linearen Algebra und Numerik
• wenden diese an, um Algorithmen aus dem Bereich der Computergraphik zu implementieren
• erstellen eigene Implementierungen,Tests und Benchmarks
• können geeignete Werkzeuge (z.B. Programmiersprache, Bibliotheken) beurteilen, sinnvoll auswählen und anwenden
• können wissenschaftlichen Artikel im Bereich Computergraphik insbesondere im Hinblick auf mathematische Inhalte verstehen und implementieren

Inhalt:

• Linear least-squares approximation
• Data interpolation and approximation
• Matrix factorization
• Sparse matrices
• Regularization
• Implementation issues
• General applications and case studies
• The specific contents may vary each semester.

Die Veranstaltung kann je Bedarf in englischer oder deutscher Sprache abgehalten werden. Vorlesungsmaterialien stehen auf englisch zur Verfügung.

Arbeitsaufwand:

• 56 h Präsenz
• 124 h selbstständige Arbeit

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
Um an der Prüfung teilnehmen zu können, sind Prüfungsvorleistungen erforderlich. Details werden in der ersten Vorlesung bekannt gegeben.	Mündliche Prüfung 20 Minuten	Vorlesung 2 SWS Übung 2 SWS

Voraussetzungen nach Prüfungsordnung:

Empfohlene Voraussetzungen:

keine

Vorlesungen im Bereich Computergraphik z.B. Grundlagen der Computergraphik CAGD Mesh Processing Spaß an mathematischen Aufgabenstellungen und Programmierung Programmierkenntnisse in C++ oder Julia oder Matlab und/oder die Bereitschaft sich schnell und selbständig in neues einzuarbeiten Grundlegende Kenntnisse der Versionsverwaltung mit Git Bereitschaft, der Veranstaltung auch in englischer Sprache zu folgen

Medienformen:	Literatur:
	Literatur wird in der Vorlesung bekannt gegeben.

Hinweise:
Wir verwenden i.d.R. Julia (https://julialang.org) als Programmiersprache (statt vormals Matlab und GNU Octave) und dazu C++ nach Bedarf. Wo genau der Fokus liegt, tarieren wir soweit möglich in der Veranstaltung je nach Vorkenntnissen und Wünschen aus.

Computational Geometry

Summer

(in German: Computational Geometry )

Module-ID: FIN-INF-102201

Abbreviation CompGeom

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr. Stefan Schirra
Lecturer:	Prof. Dr. Stefan Schirra
Classes:	Lectures and exercises Computational Geometry
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. DKE: Data Processing for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Informatik Wahlpflicht (SPO 2027) - M.Sc. VC: Visual Computing - M.Sc. VC: Visual Computing (SPO 2027)

Intended learning outcomes:
Students know how to design and analyze algorithms for advanced geometric problems. They understand the basics of combinatorial and algebraic geometry.

Content:

• basics in combinatorial geometry
• basics in algebraic geometry
• duality
• algorithm design paradigms

Workload:
56 h classroom + 124 h independent work

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
active participation	Oral exam	Lecture and Exercise (4 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Basics in algorithmic geometry (for example, bachelor modul Grundzüge der Algorithmischen Geometrie)

Media:	Literature:
	de Berg, Cheong, van Kreveld, Overmars; Computational Geometry Devadoss, O'Rourke; Computational Geometry

Comments:

Computer Graphics - an Introduction

Winter

(in German: Computer Graphics - an Introduction )

Module-ID: FIN-INF-110472

Abbreviation ICG

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Holger Theisel
Lecturer:	Holger Theisel
Classes:	Lecture Computer Graphics - an Introduction Exercise class Computer Graphics - an Introduction
Applicability in curriculum:	- M.Sc. DE: Grundlagen Informatik - M.Sc. VC: Visual Computing

Intended learning outcomes:
The students

• understand the basic concepts of computer graphics
• can apply their knowledge to create their own simple rendering program
• work in groups on exercises and programming tasks
• learn both the theoretical basics and applications of computer graphics
• are able to acquire in-depth knowledge of computer graphics after finishing the course

Content:

• Introduction, history, application areas of Computer graphics
• Modeling and acquisition of graphical data
• Transformations
• Clipping
• Rasterization and antialiasing
• Lighting
• Texturing
• Visibility
• Ray tracing
• Modern concepts of computer graphics at a glance

Workload:

• 56h contact time (lecture + exercise class)
• 94h self study (lecture material, solution of exercises and assignments)

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Successful completion of the exercises Completing a programming task	Exam: Written exam 120 min. Exam certificate: Passing the written exam 120 min.	Lectures (2 SWS) Exercises (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:
	J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes: Computer Graphics – Principles and Practice (second Edition). AddisonWesley Publishing Company, Inc., 1996 J.Encarnacao, W. D. Salomon: Computer Graphics Geometric Modeling, Springer, 1999A. Watt: 3D Computer Graphics. Addison-Wesley Publishing Company, Inc., 2000

Comments:
(nur anrechenbar wenn nicht vorher die deutschsprachige Bachelor-LV Computergraphik I belegt wurde)

Computer-Assisted Surgery

Winter

(in German: Computer-Assisted Surgery )

Module-ID: FIN-INF-120519

Abbreviation CAS

Credit Points 6

Semester Winter

Term ab 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Christian Hansen
Lecturer:	Christian Hansen
Classes:	Lecture Computer-Assisted Surgery Seminar Computer-Assisted Surgery Team project Computer-Assisted Surgery
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. VC: Visual Computing

Intended learning outcomes:
Students who complete this module ...

• have an in-depth knowledge of modern surgical and interventional imaging technologies such as iMRI, iCT and IOUS, as well as medical image processing
• understand methods such as surgical tracking, calibration and registration, which are essential for the development and application of surgical assistance systems
• can design and develop surgical planning and navigation systems.
• can write a state of the art report in the field of computer assisted surgery
• can read, analyse and briefly summarise scientific papers in a scientific report.

Within this module students can choose between a seminar or a team project. If students choose to undertake a seminar, they ...

• will be able to read, understand and present scientific publications in the field of computer-assisted surgery
• will understand how to critically analyse scientific publications and improve their ability to present the results of others' research.

If students opt for a team project, they ...

• will develop, test and evaluate a software tool in an interdisciplinary team
• will learn how to deal with conflict in interdisciplinary teams.

Content:
This lecture provides an overview of computer-assisted surgery. After an introduction to the basics, the state of the art in computer-assisted surgery is presented using clinical examples. The following topics will be covered:

• Fundamentals of intra-operative imaging
• Fundamentals of surgical visualisation
• Computer-assisted surgical planning
• Surgical navigation systems
• Surgical Augmented Reality
• Surgeon-Computer Interaction
• Robotic Surgery
• Medical Software Development and Evaluation

Workload:
56 h contact hours + 124 h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Successful completion of the exercise/seminar	Oral exam	Lecture (2SWS) Seminar (2SWS) or team project (2SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:

Flow Visualization

Summer

(in German: Flow Visualization )

Module-ID: FIN-INF-120261

Abbreviation FlowVis

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Holger Theisel
Lecturer:	Holger Theisel
Classes:	Vorlesung Flow Visualization Übung Flow Visualization
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Visual Computing

Intended learning outcomes:
The students

• understand the basic concepts of Flow Visualization in the broader context of Visualization
• are able to create their own visualization projects
• learn both the theoretical basics and applications of Flow Visualization
• are able to acquire in-depth knowledge of Flow Visualization after finishing the course

Content:

• Mathematical principles of vector and tensor fields
• Obtaining flow data
• Direct methods for flow visualization
• Texture-based methods for flow visualization
• Geometry-based methods for flow visualization
• Feature-based methods for flow visualization
• Topological methods for flow visualization
• Finite Time Lyapunov exponents
• objective methods for flow visualization

Workload:
56 contact hours + 124h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	oral exam	2 SWS lecture 2 SWS exercise class

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Einführung in die Computergraphik oder Computer Graphics - an Introduction

Media:	Literature:

Comments:

Introduction to Computer Graphics

Winter

(in German: Introduction to Computer Graphics )

Module-ID: FIN-INF-999808

Abbreviation

Credit Points 5

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Holger Theisel
Lecturer:	Holger Theisel
Classes:	Lecture Introduction to Computer Graphics Exercise Introduction to Computer Graphics
Applicability in curriculum:	- M.Sc. VC: Visual Computing

Intended learning outcomes:
The students

• understand the basic concepts of computer graphics
• can apply their knowledge to create a rendering program
• work in groups on exercises and programming tasks
• learn both the theoretical basics and applications of computer graphics
• are able to acquire in-depth knowledge of computer graphics after the course

Content:

• Introduction, history, areas of application of computer graphics
• Modeling and acquisition of graphical data
• Graphical application programming
• Transformations
• Clipping
• Rasterization and anti-aliasing
• Lighting
• Texturing
• Visibility
• Ray tracing
• Modern concepts of computer graphics at a glance

Workload:
56h contact time + 94h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	oral examination	2 SWS Lecture 2 SWS Exercise

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Einführung in die Informatik (Introduction to Computer Science)

Media:

Literature:

J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes: Computer Graphics – Principles and Practice (second Edition). AddisonWesley Publishing Company, Inc., 1996 J. Encarnacao, W. Straßer, R. Klein: Gerätetechnik, Programmierung und Anwendung graphischer Systeme, Teil I und II. Oldenbourg, München, Wien, 1966, 1997 D. Salomon: Computer Graphics Geometric Modeling, Sprin-ger, 1999 A. Watt: 3D Computer Graphics. Addison-Wesley Publishing Company, Inc., 2000

Comments:
It is not allowed to take this module and Computer Graphics - an Introduction as well.

Medical Visualization

Summer

(in German: Medical Visualization )

Module-ID: FIN-INF-102622

Abbreviation MedVis

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Bernhard Preim
Lecturer:	Gabriel Mistelbauer
Classes:	Lecture Medical Visualization Exercise class Medical Visualization
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. VC: Visual Computing

Intended learning outcomes:
Students ...

• are familiar with methods for visualizing three-dimensional medical image data
• can compare and evaluate methods for medical volume visualization
• know selected advanced methods for medical visualization
• can apply interactive methods to medical image data

Content:

• Medical volume visualization
• Visualization of 3D surfaces
• Virtual endoscopy
• Vessel visualization

Workload:
56 contact hours + 124 h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Oral exam	2 SWS lecture 2 SWS exercise class

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Lecture Visualization

Media:	Literature:
	B. Preim und D. Bartz: Visualization in Medicine, Morgan Kaufman, San Francisco, 2006Preim, Botha: Visual Computing for Medicine, 2nd Edition, , Morgan Kaufman, San Francisco, 2013

Comments:

Narrative Visualization

Summer

(in German: Narrative Visualization )

Module-ID: FIN-INF-120523

Abbreviation NarVis

Credit Points 3

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Bernhard Preim
Lecturer:	Bernhard Preim
Classes:	Seminar Narrative Visualization
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Human Factors - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Visual Computing

Intended learning outcomes:
Students ...

• can apply methods of narrative visualization for Science communication
• can design and present topics of Science communication

Content:

• Overview of Narrative Visualization
• Concepts and Tools for Story Generation
• Applications in climate research, molecular research and astronomy
• Applications in business and finance

Workload:
28 h contact hours + 152 h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Referat	2 SWS Seminar

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Module Visualization

Media:	Literature:

Comments:

Three-Dimensional & Advanced Interaction

Winter

(in German: Three-Dimensional & Advanced Interaction )

Module-ID: FIN-INF-120286

Abbreviation TAI

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Christian Hansen
Lecturer:	Florian Heinrich
Classes:	Lecture Three-Dimensional & Advanced Interaction Seminar Three-Dimensional & Advanced Interaction
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Visual Computing

Intended learning outcomes:
Students ...

• understand the nature and importance of future user interfaces and the challenges and problems associated with them
• know, analyze and evaluate technologies, interaction techniques and methods for the development of advanced user interfaces
• are able to select suitable technologies and interaction techniques in the field of three-dimensional and modern Post-WIMP user interfaces
• have the ability to critically analyze scientific literature and knowledge of scientific publishing
• can conduct own research on a postgraduate level in the field of advanced user interfaces

Content:

• Introduction to Post-WIMP and Reality-based User Interfaces
• 3DInteraction: Tasks, Devices, 3D-Widgets,
• 3D UIs Augmented Reality Interaction
• Pen-based Interaction Techniques and Sketching
• Multitouch: Technologies, Gestures, Applications
• Gestural Interaction: Tracking, Freehand Gestures
• Tangible Interaction
• Advanced Topics: Gaze-based Interaction, Organic Interfaces, Everywhere Interfaces

Workload:
56h contact time + 124h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Successful completion of the seminar (clarified in first lecture)	Written exam	Lecture (2 SWS) Exercise class (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Module Interactive Systems

Media:

Literature:

Bowman, Kruijff, Laviola, Jr., Poupyrev: „3D User Interfaces: Theory and Practice“, Addison-Wesley, 2004 Müller-Tomfelde (Ed.): „Tabletops – Horizontal Interactive Displays“, Springer, 2010 Saffer: „Designing Gestural Interfaces“, O'Reilly Media, 2008 Shaer, Hornecker: „Tangible User Interfaces: Past, Present and Future Directions“. In Foundations and Trends in Human-Computer Interaction, 3 (1), 2010

Comments:

Virtual and Augmented Reality I

Summer

(in German: Virtual and Augmented Reality I )

Module-ID: FIN-INF-120527

Abbreviation VAR1_MA

Credit Points 6

Semester Summer

Term

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Christian Hansen
Lecturer:	Christian Hansen
Classes:	Lecture Virtual and Augmented Reality Exercise class Virtual and Augmented Reality
Applicability in curriculum:	- M.Sc. VC: Visual Computing

Intended learning outcomes:
Students who complete this module will acquire in-depth knowledge in the areas of human-computer interaction in the context of VR/AR, perceptual aspects of VR/AR, VR/AR and input/output devices. In addition, they will gain an understanding of the technical fundamentals of tracking, VR/AR visualisation/rendering and AR registration. In the associated seminar, students develop, test and evaluate a VR/AR software tool in a team. Students will learn how to design, program and test a VR/AR system. In these team projects, students learn how to solve social and technical conflicts. In addition, students present their project results in an oral presentation and justify the results achieved by their team. Students who complete this module will learn how to supervise an interdisciplinary project team.  They will learn how to analyse their role in the team and their individual contribution to the team's outcome.

Content:
This module covers fundamentals and advanced techniques in the area of VR/AR systems. Students will gain the theoretical foundation needed to design, implement, improve, and evaluate VR/AR systems. In addition, the theoretical foundations can be applied in a team project that accompanies the lecture. Following topics in the field of VR/AR are addressed:

• Introduction to VR/AR systems
• Perceptional aspects
• Input devices
• Output devices
• AR components and types
• Interaction techniques

Workload:
56 contact hours + 124h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	oral exam	Lecture (2 SWS) Seminar (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
	Introduction to Computer Graphics

Media:	Literature:
	Virtual und Augmented Reality (VR/AR) Grundlagen und Methoden der Virtuellen und Augmentierten Realität herausgegeben von: Prof. Dr. Ralf Dörner, Prof. Dr. Wolfgang Broll, Prof. Dr. Paul Grimm, Prof. Dr. Bernhard Jung Verlag: Springer Berlin Heidelberg

Comments:

Visual Analytics

Summer

(in German: Visual Analytics )

Module-ID: FIN-INF-120524

Abbreviation

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Bernhard Preim
Lecturer:	Bernhard Preim
Classes:	Lecture Visual Analytics Exercise class Visual Analytics
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Visual Computing

Intended learning outcomes:
Students ...

• know machine learning methods and the visualization of their results
• apply this knowledge to analyse financial and business data
• use statistical methods to improve visual analytics solutions
• can assess and compare clustering methods

Content:

• Introduction: Potential and applications of Visual Analytics
• Visual Analytics based on clustering
• Visual Analytics based on subspace clustering and biclustering
• Visual Analytics with decision trees
• Visual Analytics with association rules
• Scatterplot-based visualizations
• Visual Analytics of events sequences
• Interaktive und Kooperative Methoden von Visual Analytics
• Visual Analytics im Gesundheitswesen

Workload:
'- 56 contact hours + 124 h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Written exam, 2 hours	2 SWS Lecture 2 SWS Exercise class

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Lecture Visualization Knowledge of Data Analysis, for example Intelligent Data Analysis Data Mining Machine Learning Artificial Intelligence

Media:

Literature:

Powerpoint presentation Whiteboard Videos

J. J. Thomas, K. A. Cook (Hrsg.): Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society 2005 D. A. Keim, F. Mansmann, J. Schneidewind, J. Thomas, H. Ziegler: Visual analytics: Scope and challenges. Visual Data Mining, 2008 Andrienko, N., Andrienko, G., Fuchs, G., Slingsby, A., Turkay, C., & Wrobel, S. (2020). Visual analytics for data scientists. Springer Nature. Kohlhammer, J., Proff, D. U., & Wiener, A. (2018). Visual business analytics: effektiver Zugang zu Daten und Informationen. dpunkt. verlag.

Comments:

Visual Analytics in Health Care

Winter

(in German: Visual Analytics in Health Care )

Module-ID: FIN-INF-120476

Abbreviation VAHC

Credit Points 3

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Bernhard Preim
Lecturer:	Bernhard Preim
Classes:	'- Seminar Visual Analytics in Health Care
Applicability in curriculum:	- M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Visual Computing

Intended learning outcomes:
The students ...

• know specific visual analytics solutions that are used in public healthcare applications
• can interpret and evaluate public healthcare data
• present and explain visual analytics solutions in healthcare scenarios

Content:

• Overview: Potential and applications of Visual Analytics in Healthcare
• Visual Analytics in Public Health
• Visual Analytics in Clinical Medicine
• Visual Analytics for Detecting Adverse Drug Effects
• Visual Analytics in Epidemiology

Workload:
3 credit points = 90 h (28 h attendance time + 62 h independent work), grading scale according to examination regulations

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Examinations: student talk, seminar paper (10 pages)	Seminar (3 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Visualization, Data Mining, Visual Analytics or Information Visualization

Media:	Literature:
PowerPoint presentation, use of whiteboard, videos	Workshop volumes of the IEEE Workshop Visual Analytics in Healthcare (since 2010), selected publications of other conferences / magazines in the fields of data analysis and visualization

Comments:
Auch anrechenbar für Studierende des Masterstudiengangs "Medical Systems Engineering".

Visualization

Winter

(in German: Visualization )

Module-ID: FIN-INF-102812

Abbreviation VIS

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Bernhard Preim
Lecturer:	Bernhard Preim
Classes:	Lecture Visualization Exercise class Visualization
Applicability in curriculum:	- M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Informatik Wahlpflicht (SPO 2027) - M.Sc. VC: Visual Computing - M.Sc. VC: Visual Computing (SPO 2027)

Intended learning outcomes:
Students ...

• understand how physiological perception affects visualization effectiveness
• know basic visualization techniques
• can generate appropriate visualizations for business and statistical data
• obtain a dataset with at least 100 observations and 5 attributes, formulate two analytical questions, and create data visualizations to answer these questions.

Content:

• Visualization goals and quality criteria
• Understanding of fundamentals of visual perception
• Overview of data structures in visualization
• Basic algorithms (Isolines, color scales, diagram techniques),
• Direct and indirect visualization of volume data
• map-based visualization
• dashboard design
• Information visualization

Workload:
56h contact time + 124h self study, students have to solve a comprehensive additional task where they analyze and visualize a multi-dimensional dataset

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Written exam, 2 hours	2 SWS Lectures 2 SWS Exercise classes

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Module Computer Graphics I Modules Mathematics I, II and III

Media:

Literature:

Powerpoint presentation Sketches Videos

P. and M. Keller (1994): Visual Cues, IEEE Computer Society PressT. Munzner (2015). Visualization Analysis and Design: Principles, Techniques, and Practice, A K Peters W. Schroeder, K. Martin, B. Lorensen (2001): The Visualization Toolkit: An object-oriented approach to 3d graphics, 3. Aufl. Springer, Heidelberg A. Telea (2014): Data Visualization: Principles and Practice, Second Edition, AK Peters (2. Auflage) M. Ward, D. Keim, G. Grinstein (2015): Interactive Data Visualization: Foundations, Techniques, and Applications, Second Edition

Comments:

Wissenschaftliches Individualprojekt

jedes

(engl. Individual Scientific Project )

Modulnummer: FIN-INF-120301

Kürzel WIP

CP 6

Semester jedes

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Studiendekan:in der FIN
Dozent:in:	Alle Dozenten der FIN
Lehrveranstaltungen:	keine
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INF: Projekte (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. WIF: Projekte (SPO 2027) - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DKE: Data Processing for Data Science - M.Sc. DKE: Applied Data Science - M.Sc. DKE: Projects (SPO 2027) - M.Sc. DE: Human Factors - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. DE: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DE: Projekte (SPO 2027) - M.Sc. VC: Visual Computing - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. VC: Projekte (SPO 2027)

Angestrebte Lernergebnisse:
Die Studierenden ...

• arbeiten selbstständig an einer wissenschaftlichen Fragestellung
• arbeiten sich selbstständig in ein wissenschaftliches Spezialgebiet ein
• erarbeiten vertiefte Kenntnisse auf einem wissenschaftlichen Spezialgebiet
• präsentieren und interpretieren ihre Ergebnisse in mündlicher und/oder schriftlicher Form
• können ihren Erfolg und die Bedeutung ihrer Ergebnisse einschätzen
• beschreiben die Auswirkungen ihrer Entscheidungen auf den Verlauf und die Ergebnisse des Projektes

Inhalt:
Die Studierenden bearbeiten in selbstständiger Arbeit eine individuell vereinbarte, wissenschaftliche Fragestellung unter Anleitung eines Hochschullehrers. Im Projekt entwickeln die Studierenden eine eigenständige Informatik-Lösung. Dadurch erwerben sie ein detailliertes und kritisches Verständnis in einem Spezialbereich der Informatik. Dieses Modul wird von unterschiedlichen Hochschullehrern implementiert. Die fachlichen Inhalte sind daher angebotsspezifisch.

Arbeitsaufwand:
180h selbstständige Arbeit

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
	Wissenschaftliches Projekt	Angeleitetes wissenschaftliches Individualprojekt

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

Hinweise:
Projekte außerhalb der FIN müssen von Studiengangsleiter abgesegnet werden.

Modules for Computer Science

AMS Lab Projects

every

(in German: AMS Lab Projects )

Module-ID: FIN-INF-120503

Abbreviation AMS-Lab

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr.-Ing. Benjamin Noack
Lecturer:	Prof. Dr.-Ing. Benjamin Noack
Classes:	AMS Lab Projects
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INF: Projekte (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. WIF: Projekte (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DKE: Projects (SPO 2027) - M.Sc. DE: Interdisziplinäres Team-Projekt - M.Sc. DE: Fachliche Spezialisierung - M.Sc. DE: Digital Engineering-Projekt - M.Sc. DE: Digital Engineering (SPO 2027) - M.Sc. DE: Engineering Specialization (SPO 2027) - M.Sc. DE: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DE: Projekte (SPO 2027) - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen - M.Sc. VC: Computer Science (SPO 2027) - M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027)

Intended learning outcomes:
Students who complete the course ...

• can collaborate in teams to solve challenges in technical domains and deepen their technical expertise.
• can manage a project and define goals and milestones
• can acquire the knowledge in a technical domain to derive solutions
• can present and document their work and achievements

Content:
This module offers different team projects and individual projects on current, research-related areas like:

• robotics
• sensor data processing
• navigation and planning
• industrial machine learning
• optimization and control
• and more

Workload:
180 h of individual and team work

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Scientific Project (Wissenschaftliches Projekt)	Project (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:
	Literature will be announced in the lecture.

Comments:

Advanced Database Models

Summer

(in German: Advanced Database Models )

Module-ID: FIN-INF-103805

Abbreviation ADBM

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Gunter Saake
Lecturer:	Eike Schallehn
Classes:	Lecture Advanced Database Models Exercise Advanced Database Models
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Data Processing for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
The students ...

• understand different non-relational database models, their basic concepts, and their historical development
• understand implications of non-relational data models for query processing and application development
• use non-relational DBMS and their specific capabilities
• develop databases and according applications using non-relational databases

Content:

• Overview and history of database models
• NF2-, object-oriented, object-relational, and semi-structured database models
• Application of the database models and design methodologies (extended ERM, UML, ODMG, XML Schema, etc.)
• Foundations of query languages (OQL, SQL:2003, XPath/XQuery, etc.) and query processing for non-relational data models

Workload:
56 h contact hours + 124 h self-study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
To be eligible to take the exam, you must have completed certain prerequisites. Further details will be announced in the first lecture.	Written exam (120 min)	Lecture 2 SWS Exercise 2 SWS

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Database introduction course

Media:	Literature:

Comments:

Advanced Programming in Computational Intelligence in Games

Summer

(in German: Advanced Programming in Computational Intelligence in Games )

Module-ID: FIN-INF-120495

Abbreviation AP-CIG

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Sanaz Mostaghim
Lecturer:	Sanaz Mostaghim
Classes:	Lecture Advanced Programming in Computational Intelligence in Games Tutorials Advanced Programming in Computational Intelligence in Games
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. VC: Computer Science

Intended learning outcomes:
Students ...

• carry out a large programming project using the concepts from the theoretical part of the course.
• have a full understanding of the algorithms of computational Intelligence in Games.
• learn to develop new ideas and apply them to new problems in the context of AI and Games.
• learn to program the methodologies from the lectures in teams.

Content:
This course addresses the basic and advanced topics in the area of computational intelligence and games and contains three parts: Part 1 addresses the basics in Evolutionary Game Theory (EGT). In this part you will learn about simple games such as scissors/rock/paper and the focus on the strategies for playing games. Part 2 is about learning agents, and we focus on reinforcement learning mechanisms. There are three questions for games:

• How can we use the information from a search mechanism to learn?
• How can we use reinforcement learning to find for a better strategy?
• How can we use reinforcement learning as a search mechanism?

The application is on board games. Part 3 contains the advanced topics in games and artificial intelligence How can we consider physical constraints of a spaceship while moving in an unknown terrain. Other topics are Monte Carlo Tree Search, Procedural Content Generation, Decision Making and Multi-Objective Learning.

Workload:
56 contact hours + 94h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Hausarbeit (home work containing programming) Exercises and Tutorials Klausur (written Exam) 120 Minuten	lectures (2 SWS) exercise classes (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Programming experience

Media:

Literature:

­Ian Millington and John Funge, Artificial Intelligence for Games, CRC Press, 2009 Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998 Jorgen W. Weibull, Evolutionary Game Theory, MIT Press, 1997 Thomas Vincent, Evolutionary Game Theory, Natural Selection, and Darwinian Dynamics, Cambridge University Press, 2005 Josef Hofbauer, Karl Sigmund, Evolutionary Games and Population Dynamics, Cambridge University Press, 1998

Comments:

Advanced Topics in Databases

Summer

(in German: Advanced Topics in Databases )

Module-ID: FIN-INF-120277

Abbreviation ATDB

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Gunter Saake
Lecturer:	David Broneske
Classes:	Lecture Advanced Topics in Databases Exercise Advanced Topics in Databases
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Data Processing for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Intended learning outcomes:
The students ...

• understand selected advanced topics in databases
• know state-of-the-art solutions for current problems in database research
• assess advanced solutions for current database systems
• can apply advanced methods in database implementation techniques

Content:
Topics of the lecture will frequently change in accordance with current research directions in the database community and represent cutting-edge aspects. Current Topics:

• Main-memory database algorithms and techniques
• Heterogeneous processing in database systems
• Advanced indexing ans storage techniques
• Storage techniques for NoSQL database systems

Workload:
56h contact hours + 124h self-study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Successful participation in the exercise	Oral exam	2 SWS Lecture 2 SWS Exercise class

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Knowledge about database foundations and about principles of internal database operations Basic programming skills

Media:	Literature:

Comments:

Advanced Topics in Deep Learning (Master)

every

(in German: Advanced Topics in Deep Learning (Master) )

Module-ID: FIN-INF-120496

Abbreviation ATDL

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Sebastian Stober
Lecturer:	Sebastian Stober
Classes:	Advanced Topics in Deep Learning
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DKE: Data Processing for Data Science - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
Students ...

• can read and analyze current research papers
• apply deep learning to an application domain
• gain in-depth knowledge In the application domain
• learn to work efficiently in a team on scientific problems
• learn to work on a server infrastructure
• present scientific results in a written and oral form

Content:

• Current topics from deep learning research
• Create domain-specific deep learning pipelines
• The application domain will be announced
  beforehand

Workload:

• 30h in person
• 150h self-study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Hausarbeit Mündliche Prüfung	Vorlesung/Seminar (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Introduction to Deep Learning

Media:	Literature:

Comments:

Advanced Topics in Networking

Winter

(in German: Advanced Topics in Networking )

Module-ID: FIN-INF-120518

Abbreviation ATN

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	David Hausheer
Lecturer:	David Hausheer
Classes:	Lecture Advanced Topics in Networking Exercise Advanced Topics in Networking
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Intended learning outcomes:
Students...

• enhance their competence in the area of networking
• understand advanced networking concepts
• can explain these concepts to other participants
• are able to apply these concepts in theoretical use cases and practical exercises
• gain expertise in practical network applications

Content:
The course deals with advanced topics in the area of networking:

• Overlay networks for content delivery, e.g., P2P, BitTorrent, CDNs, caching, overlay video streaming
• Distributed Hash Tables (DHT), e.g., Kademlia
• Blockchains
• Crypto currencies and Bitcoin
• Ethereum and smart contracts
• Secure network architectures, e.g., SCION
• Congestion Control, e.g., QUIC and Multipath-QUIC

Workload:

• 28h lectures
• 22h theoretical exercises
• 6h practical exercises
• 124h homework (further studies, practical work, preparation for the final exam)

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Prerequisites for the exam required; details will be announced at the beginning of the semester.	Written exam 120 minutes	Lecture 2 SWS Exercise 2 SWS

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Modul Computernetze

Media:	Literature:
	Textbooks as indicated. Slides and paper copies as necessary.

Comments:

Applied Discrete Modelling

Winter

(in German: Applied Discrete Modelling )

Module-ID: FIN-INF-120279

Abbreviation ADM

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	PD Dr. Claudia Krull
Lecturer:	PD Dr. Claudia Krull
Classes:	Lecture Applied Discrete Modelling Exercise class Applied Discrete Modelling
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Fachliche Spezialisierung - M.Sc. DE: Informatik Wahlpflicht (SPO 2027) - M.Sc. VC: Computer Science - M.Sc. VC: Computer Science (SPO 2027)

Intended learning outcomes:

• Students understand the basics of Markov chains and know selected applications and solution methods
• Students understand non-Markovian stochastic processes and can model and simulate them in different ways
• Students understand hidden Markovian and non-Markovian processes
• Students know selected research topics of the research group
• Students can implement the models and methods they have learned and apply them to problems from the university's main research areas, in particular medicine and engineering

Content:

• Discrete-time and continuous-time Markov chains
• Applications and implementation of solution methods for Markov chains
• Method of supplementary variables
• Proxel simulation and phase distributions
• Modeling with hidden models
• Programming solution methods for different model classes
• Modeling and solving problems from medicine and engineering

Workload:
56 h lectures and exercise classes
124 h independent work

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Solutions to Assignments 1-6 need to be handed in before the exam registration.	Oral Exam 30 Minutes	Lecture 2 SWS Exercise class 2 SWS

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Engineering mathematics Computer programming

Media:	Literature:
recorded lectures and exercises are available

Comments:

Clean Code Development

Winter

(in German: Clean Code Development )

Module-ID: FIN-INF-120432

Abbreviation CCD

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr. Frank Ortmeier
Lecturer:	Prof. Dr. Frank Ortmeier
Classes:	Vorlesung Clean Code Development Übung Clean Code Development
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen

Intended learning outcomes:
Students:

• know and understand the principles of Clean Code Development
• are able to apply the associated tools and practices in practice
• learn to independently select and adapt the concepts suitable for a project
• learn to distribute tasks within a team and collaborate in an organized manner

Content:

• Software engineering deals with the creation and development of software, the organization and modeling of related data structures, and the operation of software systems. It thus encompasses software design, implementation, and management.
• The introductory lecture Software Engineering I provided the foundational knowledge for designing and writing good software.
• This advanced lecture introduces modern techniques and methods that are commonly used in the development of large software systems.
• We align with the four core values of Clean Code Development – evolvability, correctness, production efficiency, and comprehensibility.
• To achieve these goals, a variety of programming principles and practices will be introduced.
• These include, among others, team formation and organization in software development, principles and tools of Clean Code Development, continuous integration and automated build systems, bug tracking, error localization and debugging, automated and model-based testing, code analysis and quality metrics, requirements engineering and tracing, as well as distributed and component-based software architectures.
• The course will offer a high degree of practical engagement with tools and software development techniques, especially in the exercises. The goal is not only to convey abstract knowledge but also to develop hands-on competencies.
• Participation in the exercises is mandatory and will be conducted in groups.

Workload:
56h Präsenszeit + 124h selbständige Arbeit am Projekt

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Projekt	Mündliche Prüfung	Vorlesung (2 SWS) Übung (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Software Engineering

Media:	Literature:

Comments:
Enrollment Restriction: Limited number of participants. The selection process will be announced during the first lecture.

Data Mining I - Introduction to Data Mining

Summer

Introduction to Data Mining

(in German: Data Mining I - Introduction to Data Mining )

Module-ID: FIN-INF-120454

Abbreviation DM1_ENG

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Myra Spiliopoulou
Lecturer:	Myra Spiliopoulou
Classes:	Vorlesung DM1_ENG Übung DM1_ENG COURTESY TRANSLATION: Lecture class DM1_ENG Exercise class DM1_ENG
Applicability in curriculum:	- M.Sc. DKE: Fundamentals of Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
When successfully completing this course, the students

• comprehend the purpose of model induction from data
• are familiar with the behaviour of simple learning algorithms on tabular static data and can apply them to derive models
• comprehend the purpose, challenges and basic instruments for model evaluation
• can evaluate models
• comprehend the purpose of data engineering in preparation of data mining
• are familiar with basic tasks of data engineering

and have thus acquired skills they need in order to design themselves learning algorithms, and to build workflows for model evaluation.

Content:

• Underpinnings: basic forms of learning
• BLOCK 1 - supervised: Classification algorithms and evaluation procedured
• BLOCK 2 - unsupervised: Clustering algorithms and evaluation procedures
• BLOCK Evaluation: more on model evaluation and model comparison
• BLOCK Data Engineering: basic steps on engineering the data before they are input to the model learner

Workload:

• 28 hours - in presence : lecture class (2 hours per week)
• 28 hourse in presence: exercise class (2 hours per week)
• 124 hours: independent work (for lectures, exerices and for the exam preparation)

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
A minimum number of points must be achieved during the course.	Schriftliche Prüfung - Klausur (120 min) COURTESY TRANSLATION: Written exam of the form 'Klausur' (120 min)	Vorlesung (2 SWS) Übung (2 SWS) COURTESY TRANSLATION: Lecture (2 hours per week of the semester) Exercise (2 hours per week of the semester)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Databases Data management Basics in statistics

Media:

Literature:

Book M: The book of the course is Introduction to Data Mining by Pan-Ning Tan, Michael Steinbach, Anuj Karpatne and Vipin Kumar. PEARSON, 2019 (2nd edition) Chapters 2 and 3 Parts of Chapters 4, 6, 7 Parts of Chapters 8 and 10 Overview of the book under https://www-users.cs.umn.edu/~kumar001/dmbook/index.php including slide collections etc To read the full textbook (electronic version): Find the ebook from the library here, follow the direct link, and log in -> Shibboleth login -> Otto-von-Guericke University -> Log in -> read. Note: Some slides come from the 1st edition. They concern content that did not change between the two editions. Book D: Data Preprocessing in Data Mining by Salvador Garcia, Julian Luengo and Fransisco Herrera, SPRINGER International Publishing Switzerland, 2015**** This book is used in the Block 'Data Engineering' (Block 'Data' for short). This book is available as pdfs from our library. The chapters of relevance are mentioned in the description of the course block.****

Comments:
This module is intended for students in low semesters of 4-semester-long master degrees and assumes a limited background in data science. It is not appropriate for students with a bachelor degree in data science.

Data Mining II - Advanced Topics in Data Mining

Winter

(in German: Data Mining II - Advanced Topics in Data Mining )

Module-ID: FIN-INF-120455

Abbreviation DM2

Credit Points 9

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Myra Spiliopoulou
Lecturer:	Myra Spiliopoulou
Classes:	Vorlesung DM 2 Übung DM 2 COURTESY TRANSLATION: Lecture class DM2 Exercise class DM2
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Intended learning outcomes:
When successfully completing this module, the students:

• comprehend why temporal data need different learning algorithms and evaluation procedures than used on static data
• comprehend the behaviour of supervised, unsupervised and semi-supervised learning algorithms on temporal data
• can design and apply simple learning algorithms and workflows on temporal data and interpret the induced models
• can evaluate models - once and in continuous evaluation, since both are needed in temporal learning

and have thus acquired skills they need in order to design and evaluate temporal learning algorithms themselves

Content:
Block 1A: Data Streams

• Basics
• Stream classification: learning methods and concept drift detectors; evaluation approaches
• Semi-supervised stream learning: methods and evaluation approaches

Block 1B: Time series

• Basics
• Time series classification
• Time series prediction
• Evaluation of models

Block 2: discussion of papers (inverted classroom) and hands-on of algorithms (in the exercises)

Workload:

• 28 hours - in presence : lecture class (2 hours per week)
• 28 hourse in presence: exercise class (2 hours per week)
• 214 Stunden: Individual working time on
  • Preparation for lectures and exercise classes
  • Reading and understanding the articles (scientific papers) provided for the discussion in the lecture class

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Votierung in der Vorlesung und in der Übung (A minimum number of points must be achieved in Block 1 and in Block 2)	Schriftliche Prüfung - Klausur COURTESY TRANSLATION: Written exam of the form 'Klausur' Prerequisite for the written exam: a minimum number of points must be achieved; this procedure is called 'Votierung'	Vorlesung (2 SWS) Übung (2 SWS) COURTESY TRANSLATION: Lecture (2 hours per week of the semester) Exercise (2 hours per week of the semester)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Familiarity with learning algorithms for static tabular data Familiarity with methods for the evaluation of models induced on static data

Media:	Literature:

Comments:
Block 1: Literature on streams and time series; is provided in the elearning page of the module Block 2: New papers are discussed, every time the course is offered

Data Science with Python

Winter

(in German: Data Science with Python )

Module-ID: FIN-INF-120513

Abbreviation DSWP

Credit Points 6

Semester Winter

Term

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Dr. Christian Beyer
Lecturer:	Dr. Christian Beyer
Classes:	Data Science with Python
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Interdisziplinäres Team-Projekt - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen

Intended learning outcomes:
The course is about learning from data to perform predictions and obtain useful insights. In the seminar, we will use the programming language Python.
Necessary skills to manage and analyze data will be taught and practiced on real-world applications. Programming knowledge from other courses is helpful but not mandatory. However, students are expected to have a profound knowledge of fundamental data-analysis techniques, such as classification, regression and clustering. After successful completion of this course, the student will be able to perform the following tasks in Python:

• Import and preprocess raw data (files, databases, web APIs)
• Transform data for modelling
• Perform exploratory data analysis with summary statistics and visualization
• Understand, build and evaluate predictive classification and regression models, including tree-based models, ensembles and boosted models
• Communicate and disseminate results and findings through reproducible documents, presentations, websites and interactive web applications

Content:
Part Fundamentals & Visualization: Basics, scripts, workflows, vectors & functions in Python Explorative data visualization Data transformation Part Data Management & Exploratory Data Analysis: Data cleaning & scraping Generating hypotheses and an intuition about the data with exploratory data analysis Data import Data management Relational data Strings, categorical data, dates & time Iteration: imperative & functional programming Part Modeling: Linear regression Classification Evaluation Model selection & regularization (LASSO, Ridge) Feature selection & model interpretation Decision trees Ensembles: random forests Boosting: gradient boosted trees Unsupervised learning, e.g. k-means, hierarchical clustering, self-organizing maps, principal component analysis Topic modeling with simple graphical models Statistical testing Part Communication: Communication and dissemination of results through visualization and interpretable summaries with documents, notebooks, presentations & websites

Workload:
Attendance time = 28 h: - 2 SWS weekly seminar; Independent work outside the actual Seminar time = 152 h: - 76 h preparation and follow-up of the seminar topics - 76 h solving the tasks, incl. work in the laboratory 180h = 28h attendance time + 152h independent work

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Referat	Seminar (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Area 1: Data Mining, Machine Learning, Artificial Intelligence Area 2: Databases Area 3: Programming Languages and Software Engineering Area 4: Stochastics, Applied Statistics

Media:	Literature:
	Will provided during the seminar

Comments:

Datenbanken 2

Sommer

Datenbankimplementierungstechniken

(engl. Database Implementation Techniques )

Modulnummer: FIN-INF-120531

Kürzel DB II

CP 6

Semester Sommer

Fachsem.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Gunter Saake
Dozent:in:	Gunter Saake
Lehrveranstaltungen:	Vorlesung Datenbanken 2 Übung Datenbanken 2
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Data Processing for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Angestrebte Lernergebnisse:

• Kenntnisse über die Funktionsweise von Datenbankmanagementsystemen
• Befähigung zum physischen Entwurf von Datenbanksyste-men
• Befähigung zur Administration und zum Tuning von Datenbanksystemen
• Befähigung zur Entwicklung von Komponenten von Datenbankmanagementlösungen
• Die Fähigkeit, erworbenes Wissen auf reale Datensätze zu übertragen, Implementierungsentscheidungen eigenständig zu treffen sowie die Eignung eingesetzter Algorithmen für praktische Anwendungsszenarien kritisch zu bewerten.

Inhalt:

• Aufgaben und Prinzipien von Datenbanksystemen
• Architektur von Datenbanksystemen
• Verwaltung des Hintergrundspeichers
• Dateiorganisation und Zugriffsstrukturen
• Zugriffsstrukturen für spezielle Anwendungen
• Basisalgorithmen für Datenbankoperationen
• Optimierung von Anfragen

Arbeitsaufwand:
56h Präsenz + 124h selbstständige ArbeitErfolgreiche Bearbeitung von Semesteraufgaben (Ausgabe zum Beginn des Semesters)

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
Erfolgreiche Bearbeitung von Semesteraufgaben (Ausgabe zum Beginn des Semesters)	Klausur (120 min.)	2 SWS Vorlesung 2 SWS Übung

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine	Datenbanken 1

Medienformen:	Literatur:
	Datenbanken: Implementierungstechniken. Gunter Saake, Kai-Uwe Sattler, Andreas Heuer, 4. Auflage mitp-Verlag, Bonn, 2019, ISBN: 978-3-95845-779-9

Hinweise:

Distributed Data Management

Winter

(in German: Distributed Data Management )

Module-ID: FIN-INF-102404

Abbreviation DDM

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Gunter Saake
Lecturer:	Eike Schallehn
Classes:	Lecture Distributed Data Management Exercise Distributed Data Management
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Data Processing for Data Science - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Intended learning outcomes:
The students ...

• know basic principles and advantages of distributed data management
• can implement distributed databases
• understand query and transaction processing in distributed and parallel database systems
• optimize the run-time performance and satisfy requirements regarding reliability and availability of distributed systems

Content:

• Overview and classification of distributed data management (distributed DBMS, parallel DBMS, fedrated DBMS, P2P)
• Distributed DBMS: architecture, distribution design, distributed query processing and optimization, distributed transactions, and transactional replication
• Parallel DBMS: fundamentals of parallel processing, types of parallelization in DBMS, parallel query processing

Workload:
56 h contact hours + 124 h self-study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
To be eligible to take the exam, you must have completed certain prerequisites. Further details will be announced in the first lecture.	Written exam (120 min)	Vorlesung 2 SWS Übung 2 SWS

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Database introduction course

Media:	Literature:

Comments:

Estimation for Autonomous Mobile Robots

Winter

(in German: Estimation for Autonomous Mobile Robots )

Module-ID: FIN-INF-120485

Abbreviation AMR

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr.-Ing. Benjamin Noack
Lecturer:	Prof. Dr.-Ing. Benjamin Noack
Classes:	Lecture Estimation for Autonomous Mobile Robots Exercise Estimation for Autonomous Mobile Robots
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. WIF: Informatik - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. DE: Informatik Wahlpflicht (SPO 2027) - M.Sc. DE: Digital Engineering (SPO 2027) - M.Sc. DE: Engineering Specialization (SPO 2027) - M.Sc. VC: Computer Science - M.Sc. VC: Computer Science (SPO 2027)

Intended learning outcomes:
Students who complete the course ...

• have an overview of basic problems and methods in parameter and state estimation for mobile systems.
• understand how to develop kinematic models for mobile robots and how to derive discrete-time prediction models.
• are familiar with the required mathematical tools and can derive and apply least-squares methods for localization and tracking of mobile systems, e.g., based on distance measurements.
• have a deep understanding of Kalman filtering and its nonlinear generalizations for dynamic state estimation and localization of mobile systems.

Content:

• Kinematics, System Models, and Dead Reckoning for Mobile Systems
• Sensor Models and Optimization Methods for Localization and Tracking
• DynamicState Estimation for Real-Time Localization and Tracking
• Linear Kalman Filtering and Nonlinear Generalizations

Workload:
56 h contact time 124 h independent study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Oral exam (30 min)	Lecture (2 SWS) Exercise (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Linear Algebra, Analysis

Media:	Literature:
	Literature will be announced in the lecture.

Comments:

Evolutionary Multi-Objective Optimization

Summer

(in German: Evolutionary Multi-Objective Optimization )

Module-ID: FIN-INF-120469

Abbreviation EMO

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Sanaz Mostaghim
Lecturer:	Sanaz Mostaghim
Classes:	Lecture Evolutionary Multi-Objective Optimization Exercises Evolutionary Multi-Objective Optimization
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
The students...

• have a full understanding of optimization and decision-making algorithms using evolutionary algorithms.
• are able to develop EMO algorithms
• can design optimization algorithms, model optimization problems and perform experiments.

Content:
In our daily life, we are inevitably involved in optimization. How to get to the university in the least time is a simple optimization problem that we encounter every morning. Just looking around ourselves, we can see many examples of optimization problems, even with conflicting objectives and higher complexities. It is natural to want everything to be as good as possible, in other words optimal. The difficulty arises when there are conflicts between different goals and objectives. Indeed, there are many real-world optimization problems with multiple conflicting objectives in science and industry, which are of great complexity. We call them Multi-objective Optimization Problems.
Over the past decade, many new ideas have been investigated and studied to solve such optimization problems as any new development in optimization which can lead to a better solution of a particular problem is of considerable value to science and industry. Among these methods, evolutionary algorithms are shown to be quite successful and have been applied to many applications. This course addresses the basic and advanced topics in the area of evolutionary multi-objective optimization and contains the following content:

• Introduction to single-objective optimization (SO) and multi-objective optimization (MO), classical methods for solving MO, definitions of Pareto-optimality and other theoretical foundations for MO
• Basics of evolutionary algorithms (algorithms, operators, selection mechanisms, coding and representations)
• Evolutionary multi-objective algorithms (NSGA-II, EMO scalarization methods such as MOEA/D)
• Constraint handling in SO and MO, robust optimization in EMO, surrogate methods for expensive function evaluations
• Evaluation mechanisms (Design of experiments, test problems, metrics, visualization)

Workload:
56 h contact time + 124 h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Written exam (120 Min) Exercises	2 SWS lecture 2 SWS exercise class

Prerequisites according to examination regulations:	Recommended prerequisites:
none

Media:

Literature:

Deb, Kalyanmoy. Multi-Objective Optimization Using Evolutionary Algorithms, Wiley, 2001. Coello, Carlos A. Coello, Gary B. Lamont, and David A. Van Veldhuizen. Evolutionary algorithms for solving multi-objective problems. Vol. 5. New York: Springer, 2007. Miettinen, Kaisa. Nonlinear multiobjective optimization. Vol.12. Springer Science & Business Media, 2012. Ehrgott, Matthias. Multicriteria optimization. Vol. 491. Springer Science & Business Media, 2005. Kruse, Rudolf, et al. Computational intelligence: a methodological introduction. Springer, 2016.

Comments:

Filmseminar Informatik und Ethik

Winter

(engl. Film Seminar - Computer Science and Ethics )

Modulnummer: FIN-INF-120294

Kürzel

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Gunter Saake
Dozent:in:	Eike Schallehn
Lehrveranstaltungen:	Seminar Filmseminar Informatik und Ethik
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. VC: Computer Science

Angestrebte Lernergebnisse:
Die Studierenden ...

• können sich selbstständig in ein anspruchsvolles Thema einarbeiten
• könne ein anspruchsvolles Thema präsentieren
• verstehen Fragen der Ethik des Einsatzes von Informationstechnologien

Inhalt:
Diskussion von Fragen der Ethik informationstechnischer Anwendungen, wie z.B.

• Einschränkung von Persönlichkeitsrechten
• Gesellschaftliche Effekte
• Ethische Fragen spezifischer Anwendungen (z.B. Militär, Gentechnikt, etc.)
• Sicherheit und Vertrauenswürdigkeit von Systemen am Beispiel vorgegebener und selbst gewählter Spielfilme

Arbeitsaufwand:
56h Präsenzzeit + 94h selbstständige Arbeit

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
	Referat	Seminar (4 SWS)

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine	Umfangreiche Kenntnisse von Grundlagen und Anwendungen von Informationssystemen

Medienformen:	Literatur:

Hinweise:

Grundlagen der Theoretischen Informatik III

Winter

(M.Sc.)

(engl. Introduction to the Theory of Computation III )

Modulnummer: FIN-INF-110464

Kürzel GThI 3

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Prof. Dr. Stefan Schirra
Dozent:in:	Prof. Dr. Stefan Schirra
Lehrveranstaltungen:	Vorlesung Grundlagen der Theoretischen Informatik III Übung Grundlagen der Theoretischen Informatik III
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. VC: Computer Science

Angestrebte Lernergebnisse:
Studierende lernen, formale Sprachen in die Chomsky-Hierarchie einzuordnen und beurteilen zu können. Sie verstehen algebraische Zugänge zu Formalen Sprachen. Ferner lernen sie Optionen kennen, mit schweren Problemem umzugehen, und diese anzuwenden.

Inhalt:

• Weiteres zu regulären und kontextfreien Sprachen,
• Kleene Algebren,
• Exakte Exponentialzeitalgorithmen,
• Algorithmen für spezielle Graphklassen,
• Festparameterhandhabbarkeit,
• Approximationsalgorithmen und Nichatapproximierbarkeit
• Elementare Komplexitätstheorie.

Arbeitsaufwand:
56h Präsenzzeit + 124h selbstständige Arbeit

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
Aktive Teilnahme an der Veranstaltung. Im Unterschied zur B.Sc. Variante muss mindestens einmal eine eigene Lösung zu einer Übungsaufgabe vorgestellt werden.	Klausur 120 Minuten	Vorlesung (3 SWS) Übung (1 SWS)

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine	Grundlagen der Theoretischen Informatik I + II (Bachelor)

Medienformen:	Literatur:
	Sipser; Theory of Computation Kozen; Automata and Computability Shallit; A Second Course on Automata and Computability Theory

Hinweise:

Human-Centred Manufacturing and Usability

Sommer

HCAMU

(in German: Human-Centred Manufacturing and Usability )

Module-ID: FIN-INF-120512

Abbreviation HCMU

Credit Points 6

Semester Sommer

Term

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Ernesto William De Luca
Lecturer:	Marianna Ciccarelli
Classes:	Lecture/Exercise: Human-Centred Manufacturing and Usability
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Intended learning outcomes:
The students ...

• have theoretical and practical skills for the evaluation and design of safe, efficient, and human-centered work environments
• can apply principles of Human-Centered Design and Human-Centered Manufacturing
• understand risk assessment methodologies

Content:
The course aims to provide both theoretical and practical skills for the evaluation and design of safe, efficient, and human-centered work environments. It will cover key concepts related to health and safety in industrial settings, with a particular focus on risk prevention and the integration of human resources into Industry 4.0 processes. Special attention will be given to the principles of Human-Centered Design and Human-Centered Manufacturing, with an introduction to occupational ergonomics and methods for analyzing physical and cognitive ergonomics. The course will also explore risk assessment methodologies, including applications in human-robot collaboration, as well as user-machine interaction paradigms and usability concepts. Learning activities are supported by hands-on exercises in ergonomic analysis and usability testing.

Workload:

• block course
• 40 contact hours + 140 hours self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Written report (Hausarbeit)	4 SWS Lecture/Exercise

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:

IT-Security of Cyber-Physical Systems

Winter

(engl. IT-Security of Cyber-Physical Systems )

Modulnummer: FIN-INF-120457

Kürzel ITS-CPS

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Prof. Dr.-Ing. Jana Dittmann
Dozent:in:	Prof. Dr.-Ing. Jana Dittmann
Lehrveranstaltungen:	Vorlesung IT-Security of Cyber-Physical Systems Projekt IT-Security of Cyber-Physical Systems
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. VC: Computer Science - M.Sc. VC: Computer Science (SPO 2027)

Angestrebte Lernergebnisse:
Die Studierenden

• kennen und verstehen das Zusammenspiel von System-, Netzwerk- und Anwendungssicherheit
• können IT-Sicherheit zu einer technischen Anwendung im Security-by-Design aus der Forschung selbständig bewältigen
• können bestehende Ansätze bewerten und kreativ für eine Forschungsfrage erweitern
• können sich in den technischen Anwendungsfall einarbeiten und im Team reflektieren
• können für die technische Fragestellung neue Ansätze zur IT-Sicherheit aufzeigen
• können für den Anwendungsfall die IT-Sicherheitsbedarfe feststellen und Verbesserungsalternativen vorschlagen
• können eine ausgewählte technische Lösung umsetzen, evaluieren und bewerten sowie Restrisiken aufzeigen
• können selbständig wissenschaftliche Methoden der IT-Sicherheit anwenden
• können die Ergebnisse wissenschaftlich in einem Referat präsentieren und sich einer Diskussion stellen

Inhalt:
Aktuelle Herausforderungen und Erarbeitung von Lösungen zur IT Sicherheit am Beispiel von einem technischen Anwendungsfall:

• ausgewählten Grundlagen von System-, Netzwerk- und Anwendungssicherheit
• Sicherheitaspekte von Bussystemen
• Design und Realisierung hardwarenaher Sicherheitslösungen
• Bewertung sicherer Systeme mittels Evaluierung
• Wiederholung: Grundlagen der Teamarbeit, Vorlesung ist Projekt-orientiert

Arbeitsaufwand:
Projektvorlesung zu ausgewählten technischen Themen der IT Sicherheit; Vergabe eines anspruchsvollen Themas zu selbständigen Bearbeitung und Lösung einer gestellten Aufgabe 4 SWS = 2V + 2Ü (Labor) Arbeitsaufwand: 180h (56 h Präsenzzeit + 124 h selbstständige Arbeit)

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
keine	Referat mit zwei Teilleistungen (Vortrag und Ausarbeitung): Das Referat umfasst eine eigenständige und vertiefte schriftliche Auseinandersetzung mit einer Fragestellung aus dem Arbeitszusammenhang der Lehrveranstaltung unter Einbeziehung und gezielter kritischer Auswertung einschlägiger Literatur, sowie die Darstellung der eigenen Arbeit auch im Vergleich im Team und die Vermittlung sowie Bewertung der eigenen Ergebnisse im mündlichen Vortrag sowie in der anschließenden Diskussion im Team. Die Ausarbeitungen müssen schriftlich vorliegen.	Vorlesung (2 SWS) Projekt (2 SWS)

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine	Sichere Systeme, Algorithmen und Datenstrukturen, Grundlagen der Technischen Informatik

Medienformen:	Literatur:

Hinweise:
https://omen.cs.uni-magdeburg.de/itiamsl/deutsch/lehre/index.html

In-Memory and Cloud-Technologies

Winter

(in German: In-Memory und Cloud-Technologien )

Module-ID: FIN-INF-120506

Abbreviation IMCloud

Credit Points 6

Semester Winter

Term starting at 1.

Duration 2 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Hon. Prof. Dr. Alexander Zeier
Lecturer:	Hon. Prof. Dr. Alexander Zeier
Classes:	Vorlesung In-Memory und Cloud-Technologien 1 Vorlesung In-Memory und Cloud-Technologien 2
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Data Processing for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
Die Studierenden ...

• können Methoden ihres Fachs einschätzen und kreativ weiterentwickeln.
• können sich neues Wissen eigenständig aneignen und für sich nutzbar machen.
• können bei unbekannten Aufgaben eigene Ideen einbringen und Entscheidungen treffen.
• können für neue Aufgaben geeignete Lösungen wählen, anwenden und anschließend beurteilen.
• können selbstständig wissenschaftlich arbeiten.
• haben ein Verständnis für ausgewählte Vertiefungsgebiete der Wirtschaftswissenschaften, der Wirtschaftsinformatik und der Informatik und für ihre Zusammenhänge.
• können existierende Methoden von Data Science für neuartige Probleme anpassen.
• haben vertiefte, spezialisierte Kenntnisse in der Informatik und können diese in ingenieurwissenschaftlichen Domänen anwenden.
• können in interdisziplinären Projekten Digitalisierungslösungen für komplexe ingenieurwissenschaftliche Systeme beitragen.

Content:
In-Memory Technologie und Anwendungen mit Focus auf SAP HANA:

• Erläuterung der In-Memory-Technologie mit Focus auf SAP HANA
• Zeilen- versus Spaltenhauptspeicherdatenbanken
• Komprimierungs-, Partitionierungs- und Indexierungsansätze
• Google Cloud Technologie und Services
• Einsatz z.B. von Anthos, Bigquery, und AutoML
• Entwicklung von Hochverfügbarkeitslösungen und Backupstrategien
• Erweiterung des Datenlayouts ohne Downtime
• Migrationsansätze für Projekte in denen In-Memory Datenbanken eingesetzt werden

Workload:
Präsenzzeiten = 56 h:

• 56 h Vorlesung
• Selbstständiges Arbeiten = 64 h:
• 60 h Vorbereitung auf die Vorlesung und Lesen der empfohlenen Literatur

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Eine mündliche Prüfung am Ende des Kurses	Vorlesung (4SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Datenbanken I Datenbanken II

Media:

Literature:

Plattner, H., Zeier, A.: In-Memory Data Management: Technology and Applications, Springer Verlag, 2. Auflage, Mai 2012, ISBN 978-3642295744 Whitepaper HANA on Intel: Three Steps to Reinvent Your Enterprise as a Digital Disrupter†von Prof. Dr. Alexander Zeier & Intel CTO Enterprise Ed Goldman, 2016. Cloud Computing, Blog (July 2020) zu Digital Decoupling. Title: Trapped by legacy systems, CIOs look for a way out https://www.accenture.com/us-en/blogs/cloud-computing/zeier-digitaldecoupling-sap-google-cloud

Comments:
Start jeweils zum Wintersemester.

Introduction to Deep Learning

Summer

(in German: Introduction to Deep Learning )

Module-ID: FIN-INF-120521

Abbreviation IDL

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Sebastian Stober
Lecturer:	Sebastian Stober
Classes:	Lecture Introduction to Deep Learning Exercise class Introduction to Deep Learning
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
Students ...

• confidently apply DL techniques to develop a solution for a given problem
• follow recent DL publications and critically assess their contributions
• formulate hypotheses and design & conduct DL experiments to validate them
• document progress & design decisions for reproducibility and transparency
• have advanced competencies in scientific research in topics of the module

Content:

• Artificial neural network fundamentals (gradient descent & backpropagation, activation functions)
• Network architectures (Convolutional Neural Networks, Recurrent/Recursive Neural Networks, Auto-Encoders, Transformers)
• Regularization techniques
• Introspection & analysis techniques
• Optimization techniques
• Advanced training strategies (e.g. teacher-student)

Workload:

• 56h contact hours (2 SWS lecture + 2 SWS exercise groups)
• 124h self study (reading assignments (flipped classroom), programming exercises)

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Exam requirements: participation and active involvement in the course and the exercises (defined in the 1st lecture and published on the course website) Final exam: written (120 minutes) Schein: pass final exam (at least 4.0)	Lecture (2 SWS) Exercise (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Machine Learning (required)

Media:	Literature:
	Ian Goodfellow, Yoshua Bengio & Aaron Courville: "Deep Learning", MIT Press, 2016.

Comments:

Introduction to Distributed Sensor Data Fusion

Summer

(in German: Introduction to Distributed Sensor Data Fusion )

Module-ID: FIN-INF-120497

Abbreviation SDF

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr.-Ing. Benjamin Noack
Lecturer:	Prof. Dr.-Ing. Benjamin Noack
Classes:	Lecture Introduction to Distributed Sensor Data Fusion Exercise Introduction to Distributed Sensor Data Fusion
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. WIF: Informatik - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science - M.Sc. VC: Computer Science (SPO 2027)

Intended learning outcomes:
Students who complete the course ...

• have an overview of basic problems and methods in designing distributed sensor systems and their applications.
• understand how to process data in a network of sensors, what requirements the infrastructure must meet, and how to model and describe errors like measurement noise.
• are familiar with the mathematical tools and can apply them.
• can analyze, compare, and evaluate different approaches to information processing of sensor data.

Content:
This lecture introduces basic principles, requirements, and methods of sensor data processing. Since data are more often gathered by networked sensor systems, this lecture places particular emphasis on distributed sensor data fusion methods. We will start by discussing the technical specifications of a sensor system and the basics of digital sensor data processing. Our study includes sampling theorems, compressive sensing, and signal matching. We will consider the required infrastructure to processing sensor data in networked systems, i.e., sensor networks. Based on this infrastructure, we can apply methods for multisensor data fusion to spatially distributed sensors and can monitor spatio-temporal processes.

Workload:
56 h contact time 124 h independent study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Oral exam (30 min)	Lecture (2 SWS) Exercise (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
none

Media:	Literature:
	Literature will be announced in the lecture.

Comments:

Introduction to Machine Learning Safety

Sommer

(in German: Introduction to Machine Learning Safety )

Module-ID: FIN-INF-120532

Abbreviation MLS

Credit Points 6

Semester Sommer

Term ab 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr. Frank Ortmeier
Lecturer:	Prof. Dr. Frank Ortmeier, Konstantin Kirchheim
Classes:	Vorlesung Introduction to Machine Learning Safety Übung Introduction to Machine Learning Safety
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:

• Verständnis grundlegender Unterschiede zwischen klassischer Softwareentwicklung und datengetriebener Entwicklung von ML-Systemen sowie der daraus resultierenden Sicherheitsimplikationen.
• Fähigkeit, typische Fehlermodi von ML-Systemen systematisch zu analysieren und deren Ursachen (z. B. Datenverteilung, Modellunsicherheit, Verteilungsverschiebung) zu identifizieren.
• Kompetenz, geeignete Test- und Evaluationsverfahren für ML-Modelle in sicherheitskritischen Kontexten auszuwählen und anzuwenden.
• Einordnung und Bewertung von Unsicherheitsabschätzungen, Anomalieerkennung und Robustheitsmechanismen im Hinblick auf ihre sicherheitstechnische Relevanz.
• Verständnis adversarieller Bedrohungsmodelle sowie grundlegender Verteidigungsstrategien.
• Reflexion regulatorischer, ethischer und gesellschaftlicher Rahmenbedingungen beim Einsatz lernbasierter Systeme.

Content:

• Grundlagen und Sicherheitskontext
• Datenqualität und Modelltestverfahren
• Robustheit und Angriffsmodelle
• Unsicherheit und Anomalieerkennung
• Alignment und gesellschaftliche Aspekte

Workload:

• 20h Übungsteilnahme,
• 20h Vorlesung,
• 40h Vorlesungsvorbereitung,
• 40h Übung,
• 60h Prüfungsvorbereitung

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Teilnahme an der Übung	Hausarbeit	Übung (2 SWS) Vorlesung (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Introduction to Deep Learning

Media:	Literature:

Comments:

Learning Generative Models

Winter

(in German: Learning Generative Models )

Module-ID: FIN-INF-120522

Abbreviation LGM

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Sebastian Stober
Lecturer:	Sebastian Stober
Classes:	Lecture Learning Generative Models Exercise class Learning Generative Models
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
Students can ..

• confidently apply generative models to develop a solution for a given problem
• follow recent publications on generative models and critically assess their contributions
• formulate hypotheses and design & conduct experiments with generative models to validate them
• document progress & design decisions for reproducibility and transparency

Content:

• Training methods & architectures for generative models, in particular energy-based models (e.g. Restricted Boltzmann Machines, RBMs)
• autoregressive models
• variational learning and Generative Adversarial Nets (GANs)
• flow-based models
• diffusion models

Workload:
56 contact hours + 124h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Oral exam (necessary preliminary work for admission to the examination is announced in the first week of the course) Schein (necessary preliminary work for admission to the examination is announced in the first week of the course)	2 SWS lecture 2 SWS exercise class

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Introduction to Deep Learning or Deep Learning für Ingenieure (required)

Media:	Literature:
	Ian Goodfellow, Yoshua Bengio & Aaron Courville: “Deep Learning”, MIT Press, 2016.

Comments:

Logik 2

Winter

(engl. Logic 2 )

Modulnummer: FIN-INF-0

Kürzel Logik2

CP 10

Semester Winter

Fachsem. ab 4.

Dauer 1 Semester

Sprache deutsch

Niveau Bachelor,Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Fabian Neuhaus
Dozent:in:	Fabian Neuhaus
Lehrveranstaltungen:	Vorlesung Logik 2 Übung Logik 2
Verwendbarkeit:	- B.Sc. INF: Informatik - Wahlpflicht - B.Sc. INF: Informatik Wahlpflicht (SPO 2027) - B.Sc. CV: Informatik - Wahlpflicht - B.Sc. CV: Computervisualistik Wahlpflicht (SPO 2027) - B.Sc. INGINF: Informatik - Wahlpflicht - B.Sc. INGINF: Informatik Wahlpflicht (SPO 2027) - B.Sc. WIF: Verstehen und Gestalten - Wahlpflicht - B.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - B.Sc. INF (bilingual): Informatik - Wahlpflicht - B.Sc. INF (bilingual): Informatik Wahlpflicht (SPO 2027) - M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. DKE: Fundamentals of Data Science - M.Sc. DE: Grundlagen Informatik - M.Sc. DE: Informatik Grundlagen (SPO 2027) - M.Sc. VC: Computer Science - M.Sc. VC: Computer Science (SPO 2027)

Angestrebte Lernergebnisse:

• Anwendungen von ausgewählten Erweiterungen der klassischen Logik zur Wissensrepräsentation
• Anwendungen von ausgewählten nicht-klassischen Logiken zur Wissensrepräsentation
• Anwendung von Beweiskalkülen für Logiken
• Verstehen von fortgeschrittenen semantischen Methoden für Logiken
• Verstehen von metalogischen Beweisen und die Fähigkeit einfache metalogische Beweise selbst zu führen

Inhalt:
Die Veranstaltung baut auf der Pflichtveranstaltung Logik auf, in der die klassische Aussagen- und Prädikatenlogik eingeführt wird. In Logik 2 werden einerseits weiterführende Themen der klassischen Logik behandelt (u.a. Korrektheits-, Vollständigkeits- und Kompaktheitsbeweise). Darüber hinaus werden Erweiterungen der klassischen Logik sowie nichtklassische Logiken behandelt, z.B. Modallogik, Temporallogik, epistemische Logik, Konditionallogik, mehrwertige Logiken und Logik höherer Stufe.

Arbeitsaufwand:

• Präsenzzeiten: 14 X 4h = 56h
• selbständige Arbeit = 244h
  (Vorbereiten der Vorlesung,
  Lösen Übungsaufgaben,
  Vorbereitung Präsentation)

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
2 Drittel der Übungsaufgaben votiert BA: mindestens 1 Präsentation MA: mindestens 2 Präsentationen	Mündliche Prüfung	Übung 2 SWS Vorlesung 2 SWS

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine	Erfolgreicher Abschluss des Modul Logik bzw. fundiertes Wissen im Bereich der Themen des Moduls"Logik.

Medienformen:	Literatur:

Hinweise:
Anmeldung via E-Learning-Plattform

MLOps for Small Language Model Applications

Winter

(in German: MLOps for Small Language Model Applications )

Module-ID: FIN-INF-120515

Abbreviation LLMOps

Credit Points 9

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr. Klaus Turowski, Dr.-Ing. Abdulrahman Nahhas
Lecturer:	Prof. Dr. Klaus Turowski, Dr.-Ing. Abdulrahman Nahhas
Classes:	Vorlesung + Übung + Tutorial
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
Students ...

• know and understand specialized subject content that expands and deepens Large Language Models (LLM) and machine learning development and operation.
• can independently analyze problems and identify alternative solutions.
• know how to work with large language models following proper machine learning development and operation practices.
• are experienced in the theory and handling of container-based virtualization technologies such as Docker.
• know how to use higher programming languages (Python).
• know how to use various configuration and automation tools and languages to automate the development and operation of LLM solutions.
• have practical and theoretical knowledge of testing LLM-solutions, particularly in relation to essential DevOps concepts such as Continuous Integration and Continuous Delivery, LLMOps, testing, and system landscape.
• have the ability to design, create, test, commission, and support LLM solutions for business problems.

Content:
The generalization and standardization of the Large Language Model Operations (LLMOps) life cycle is crucial for the effective adoption and management of Large Language Models (LLMs) in a business context. In many cases, the lifecycle of an LLM solution does not end with the delivery an initial solution; rather, subsequent model improvement, operation, monitoring, and maintenance have become essential parts of this process. At this point, sophisticated paradigms and methods are required to facilitate the continuous development and operation of these systems while preventing errors, failures, and other disruptions. The course is designed to teach the fundamentals, as well as provide initial practical experience in the development of LLM solutions and technologies, in conjunction with dedicated cloud-native technologies and an emphasis on small LLMs (SML) that are edge-suitable, may be operated on premise, or are ressource-efficient. In addition to the theoretical basics, essential concepts and technologies are discussed and applied that enable the continuous improvement, integration, delivery, and testing of LLM solutions for business problems.

Workload:
Attendance:
21 h lecture / 63 h exercise Independent work: 207 h

1. Preparation and follow-up of lecture & exercise
2. Development of an LLM-based sotution following LLMLOps practicies to address a business problems

9 x30h (63 h attendance time + 207 h independent work)

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Report	Vorlesung (2 SWS) Übung (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
VLBA: Systemarchitectures or 2. VLBA Cloud DevOps Technologies or 3. Version control systems, DevOps practicies (CI/CD), Containerization technologies, and python.	VLBA: Systemarchitectures or 2. VLBA Cloud DevOps Technologies or 3. Version control systems, DevOps practicies (CI/CD), Containerization technologies, and python.

Media:	Literature:

Comments:

Machine Learning

Winter

(in German: Machine Learning )

Module-ID: FIN-INF-120517

Abbreviation ML

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Andreas Nürnberger
Lecturer:	Andreas Nürnberger
Classes:	Lecture Machine Learning Exercise class Machine Learning
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Fundamentals of Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
Students ...

• can create machine learning pipelines and individual algorithms
• program decision trees, multi-layer perceptrons, and KNN classifiers in Java, Python, C++
• can evaluate the predictive performance of various classifiers for practical scenarios

Content:

• Introduction to concept spaces and concept learning
• Algorithms for instance-based learning and cluster analysis
• Algorithms for generating decision trees
• Bayesian learning
• Neural networks
• Association learning
• Reinforcement learning
• Hypothesis evaluation

Workload:
56 contact hours + 124h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Written exam (also for pass/fail grade). Requirements for exam participation will be announced in the first week of the course in class and online.	Lecture (2 SWS) Exercise class (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Algorithmen und Datenstrukturen

Media:	Literature:

Comments:
Can only be credited if the module "Grundlagen des Maschinellen Lernens" has not been credited already.

Management of Global Large IT-Systems in International Companies

Summer

(in German: Management of Global Large IT-Systems in International Companies )

Module-ID: FIN-INF-120315

Abbreviation MGLIIC

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Gunter Saake
Lecturer:	Karl-Albert Bebber, Wolfgang Stockhausen
Classes:	Block lecture Management of Global Large IT-Systems in International Companies Block Exercise class Management of Global Large IT-Systems in International Companies
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Human Factors - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Intended learning outcomes:
Students are able to …

• understand the fundamentals of IT strategy and analyze its significance for businesses.
• describe methods for developing and implementing IT strategies.
• apply project management methodologies such as PRINCE2 and agile approaches in IT projects.
• understand multi-project management, program management and portfolio management in corporate environments.
• apply change management models such as ADKAR to manage organizational transformations.
• understand the importance of IT governance and compliance for corporate structures and regulatory requirements.
• describe stakeholder management and intercultural communication in global IT projects.
• identify risks and quality requirements in IT projects and develop appropriate control measures.
• explain data governance concepts and assess their significance for data protection, IT security, and regulatory compliance.
• describe the role of data quality, metadata management and master data management (MDM) in corporate IT.
• model, analyze, and optimize IT-supported business processes, particularly through the use of BPMN.
• understand Identity & Access Management (IAM) and evaluate its implementation in enterprises.
• explain security aspects such as authentication, authorization, Single Sign-On (SSO), and two-factor authentication (2FA), as well as best practices in enterprise security management.
• assess IT security strategies and cybersecurity measures, including risk mitigation techniques.
• understand the importance of reference data management and data integration for corporate processes.
• evaluate agile methodologies such as Scrum in IT transformation projects and compare them with traditional waterfall approaches.
• understand risk and quality management processes in IT environments, including testing methodologies, compliance frameworks, and risk mitigation strategies.
• analyze and apply IT service delivery models in Shared Service Centers.

Content:

• Business Process Management and Modeling
• Process Analysis and Optimization/Standardization
• Identity and Access Management (IAM)
• Quality and Risk Management in IT Operations
• Testing and Software Quality Assurance, including Documentation
• Agile vs. Waterfall Project Management in IT
• IT Service Delivery and Shared Service Center Management
• Project and Program Management (PRINCE2 i. a.)
• Stakeholder Management in IT Projects
• Change Management (ADKAR, Organizational and Employee Transformation)
• Data Governance (Strategy, Policies, Processes and Data Quality)
• IT Strategy – Business as a Driver for IT (Balanced Scorecard)
• IT Investments and Business Decision-Making
• Insights from Ongoing IT Projects by Public Accountants and IT Project Leads from Major International Companies

Workload:
56 contact hours + 124 h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Requirements for exam admission will be announced in the first lecture	Written Exam (120 min)	2 SWS Blockvorlesung 2 SWS Blockübung

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Knowledge about IT-Systems and Business administration

Media:	Literature:

Comments:

Multimedia and Security

Sommer

(engl. Multimedia and Security )

Modulnummer: FIN-INF-120458

Kürzel MMSEC

CP 6

Semester Sommer

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Prof. Dr. Jana Dittmann
Dozent:in:	Prof. Dr. Jana Dittmann
Lehrveranstaltungen:	Vorlesung Mediensicherheit Übung Mediensicherheit
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. VC: Computer Science - M.Sc. VC: Computer Science (SPO 2027)

Angestrebte Lernergebnisse:
Die Studierenden ...

• kennen und verstehen Aspekte der Mediensicherheit
• können ausgewählte Forschungsfragen der Mediensicherheit selbständig bewältigen
• können bestehende Ansätze bewerten und kreativ für eine Forschungsfrage erweitern
• können sich in den semesterspezifischen Anwendungsfall einarbeiten und im Team reflektieren
• können für die Fragestellung neue Ansätze zur Mediensicherheit aufzeigen
• können für den semesterspezifischen Anwendungsfall Sicherheitsbedarfe feststellen und Verbesserungsalternativen vorschlagen
• können eine ausgewählte Lösung umsetzen, evaluieren und bewerten sowie Restrisiken aufzeigen
• können selbständig wissenschaftliche Methoden der IT-Sicherheit anwenden
• können die Ergebnisse wissenschaftlich in einem Referat präsentieren und sich einer Diskussion stellen

Inhalt:

• Motivation, Einführung und Grundlagen zu ausgewählten Themen der Mediensicherheit mit aktuellen Forschungsfragen und Semester-spezifischen Anwendungsfällen
• Zusammenfassung von Sicherheitsaspekten in Multimedia­anwendungen und Multimedia-spezifische Umsetzungen von Sicherheitsprotokollen und -mechanismen für Bild, Video und Audio sowie Kombinationen mit Fokus auf die Semester-spezifischen Anwendungsfälle
• Erarbeitung, Umsetzung, Evaluierung und Bewertung von Lösungsmöglichkeiten für die Semester-spezifischen Anwendungsfälle
• Wiederholung: Grundlagen der Teamarbeit, Vorlesung ist Projekt-orientiert

Arbeitsaufwand:
56 h Präsenzzeit + 124 h selbstständige Arbeit

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
keine	Referat Das Referat umfasst eine eigenständige und vertiefte schriftliche Auseinandersetzung mit einer Fragestellung aus dem Arbeitszusammenhang der Lehrveranstaltung unter Einbeziehung und gezielter kritischer Auswertung einschlägiger Literatur, sowie die Darstellung der eigenen Arbeit auch im Vergleich im Team und die Vermittlung sowie Bewertung der eigenen Ergebnisse im mündlichen Vortrag sowie in der anschließenden Diskussion im Team. Die Ausarbeitungen müssen schriftlich vorliegen.	Vorlesung (2 SWS) Übung (2 SWS)

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

Hinweise:
https://omen.cs.uni-magdeburg.de/itiamsl/deutsch/lehre/index.html

Musik Information Retrieval

Winter

(engl. Music Information Retrieval )

Modulnummer: FIN-INF-120516

Kürzel MIR

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Sebastian Stober
Dozent:in:	Sebastian Stober
Lehrveranstaltungen:	Vorlesung Musik Information Retrieval Übung Musik Information Retrieval
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Angestrebte Lernergebnisse:
Die Studierenden ...

• kennen die Grundlagen der digitalen Musikverarbeitung
• kennen unterschiedliche Musikformate (Repräsentationen) und relevanter Merkmale
• können Probleme der Musikanalyse mit Hilfe von Theorien und Methoden der Informatik analysieren und modellieren
• können computergestützte Lösungen für die Analyse von Musikdaten umzusetzen und erproben (u.a. Musiksynchronisation, Strukturanalyse, Content-Based Retrieval, Quellentrennung)

Inhalt:

• Music Representations
• Fourier Analysis of Signals
• Music Synchronization
• Music Structure Analysis
• Chord Recognition
• Tempo and Beat Tracking
• Content-Based Audio Retrieval
• Musically Informed Audio Decomposition

Arbeitsaufwand:

• Präsenzzeit = 56 Stunden
• Selbstständige Arbeit = 124 Stunden: Vor- und Nachbearbeitung von Vorlesung und Übung, Bearbeiten von Übungs- und Programmieraufgaben, Kursprojekt

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
	Prüfung in mündlicher Form: Ankündigung der notwendigen Vorleistungen in der ersten Veranstaltungswoche und auf der Vorlesungswebseite; Schein (mündlich): Ankündigung der notwendigen Vorleistungen in der ersten Veranstaltungswoche und auf der Vorlesungswebseite	Vorlesung (2 SWS) Übung (2 SWS)

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:
	Meinard Müller: Fundamentals of Music Processing Using Python and Jupyter Notebooks 2nd edition, Springer 2021 ISBN: 978-3-030-69807-2

Hinweise:

Neuro-Symbolic Integration

Winter

(in German: Neuro-Symbolic Integration )

Module-ID: FIN-INF-0

Abbreviation NeSy

Credit Points 6

Semester Winter

Term

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Martin Glauer
Lecturer:	Martin Glauer
Classes:	Lectures Exercise Classes
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Informatik Wahlpflicht (SPO 2027) - M.Sc. VC: Computer Science - M.Sc. VC: Computer Science (SPO 2027)

Intended learning outcomes:
Die Absolvent:innen vertiefen die Inhalte der Lehrveranstaltungen Logik und Intelligente Systeme, indem sie lernen, wie diese zu Neuro-Symbolischen Methoden kombiniert werden können. Hierfür werden neuste Forschungsinhalte aus diesem Gebiet vorgestellt und hinsichtlich der Eignung bestimmter Problemstellungen untersucht. Somit lernen Absolvent:innen komplexe Aufgaben aus der Schnittstelle von Anwendung und Forschung selbstständig zu bewältigen. In den Übungen werden auch offene Aufgaben gestellt, bei denen die eigenen Ergebnisse erklärt und kritisch bewertet und ggf alternative Ansätze vorgeschlagen werden sollen. Die Teilnehmenden können so eigene Ideen einbringen und erlangen detailliertes und kritisches Verständnis der Methoden der neuro-symbolischen Integration.

Content:

• (Fuzzy) Logic
• Neuro-Symbolic Integration nach von Hamelen
• Propositional Program Extraction (Arthur, KBANN)
• Cognitive architectures (CLARION)
• Logic Tensor Networks
• Logical Neural Networks
• Graph Neural Networks
• Ontology-based Training

Workload:
Total: 180 h

• Weekly lecture (2 SWS = 28 h)
  • Follow-up work (20 h)
• Weekly exercise session (2 SWS = 28 h)
  • Completing assignments (84 h)
  • Follow-up work (20 h)

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Active participations during the exercise sessions Voting for exercise tasks	Schriftliche Prüfung/Klausur bzw. elektronische Prüfung (written exam)	Lecture (2 SWS) Exercise (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Logik/Logic Intelligente Systeme/Intelligent Systems Deep Learning Programming experience (e.g. in Python)

Media:

Literature:

Books P. Hitzler and M. K. Sarker (eds.): Neuro-Symbolic Artificial Intelligence, IOS Press, 2022 Artur S. d'Avila Garcez, Krysia Broda, Dov M. Gabbay: Neural-symbolic learning systems - foundations and applications. Perspectives in neural computing, Springer 2002  PDF Garcez A, Lamb LC, Gabbay DM. Neural-symbolic cognitive reasoning. Springer; 2009. Cognitive Technologies. Hammer B, Hitzler P, editors. Perspectives of neural-symbolic integration. Springer; 2007. Survey papers Michael van Bekkum, Maaike de Boer, Frank van Harmelen, André Meyer-Vitali, Annette ten Teije: Modular design patterns for hybrid learning and reasoning systems. Appl. Intell. 51(9): 6528-6546 (2021) Md. Kamruzzaman Sarker, Lu Zhou, Aaron Eberhart, Pascal Hitzler: Neuro-Symbolic Artificial Intelligence: Current Trends. CoRR abs/2105.05330 (2021) Artur d'Avila Garcez, Luís C. Lamb: Neurosymbolic AI: The 3rd Wave. CoRR abs/2012.05876 (2020) Tarek R. Besold, et al.: Neural-Symbolic Learning and Reasoning: A Survey and Interpretation. CoRR abs/1711.03902 (2017) Marra G, Dumancic S, Manhaeve R, et al. From statistical relational to neural symbolic artificial in- telligence: a survey. CoRR abs/2108.11451 (2021) Grady Booch et al.: Thinking Fast and Slow in AI. AAAI 2021: 15042-15046 (2021) Specific approaches Samy Badreddine, Artur d'Avila Garcez, Luciano Serafini, Michael Spranger: Logic Tensor Networks. Artif. Intell. 303: 103649 (2022) Ryan Riegel et al.: Logical Neural Networks. CoRR abs/2006.13155 (2020) Honghua Dong et al.: Neural Logic Machines. ICLR (Poster) 2019. Also: CoRR abs/1904.11694 Matthieu Zimmer et al.: Differentiable Logic Machines. CoRR abs/2102.11529 (2021) F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini. “The graph neural network model.” In: IEEE transactions on neural networks 20.1 (2008), pp. 61–80. J. Chen, P. Hu, E. Jiménez-Ruiz, O. M. Holter, D. Antonyrajah, and I. Horrocks. “OWL2Vec*: embedding of OWL ontologies.” In: Mach. Learn. 110.7 (2021), pp. 1813–1845. Aws Albarghouthi: Introduction to Neural Network Verification.

Comments:

Praktikum IT Sicherheit

jedes

(engl. Praktikum IT-Security )

Modulnummer: FIN-INF-120291

Kürzel P-ITSEC

CP 6

Semester jedes

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Prof. Dr.-Ing. Jana Dittmann
Dozent:in:	PD Dr.-Ing. habil. Christian Krätzer
Lehrveranstaltungen:	Wissenschaftliches Projekt Praktikum IT-Sicherheit
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Interdisziplinäres Team-Projekt - M.Sc. DE: Digital Engineering-Projekt - M.Sc. VC: Computer Science - M.Sc. VC: Computer Science (SPO 2027)

Angestrebte Lernergebnisse:
Die Studierenden ...

• kennen und verstehen Sicherheitsaspekte des ausgewählten Themas
• können die ausgewählte Forschungsfragen selbständig bewältigen
• können bestehende Ansätze bewerten und kreativ für eine Forschungsfrage erweitern
• können sich in Sicherheitsaspekte des ausgewählten Themas einarbeiten und reflektieren
• können für die Fragestellung neue Ansätze für das ausgewählten Thema aufzeigen
• können für das ausgewählte Thema Sicherheitsbedarfe feststellen und Verbesserungsalternativen vorschlagen
• können eine ausgewählte Lösung umsetzen, evaluieren und bewerten sowie Restrisiken aufzeigen
• können selbständig wissenschaftliche Methoden der IT-Sicherheit anwenden
• können die Ergebnisse wissenschaftlich in einem Referat präsentieren und sich einer Diskussion stellen
• können Herausforderungen allein oder ggf. in der Teamarbeit erkennen und reflektieren sowie angehen

Inhalt:
Praktikum als Wissenschaftliches Projekt mit Erarbeitung eines Demonstrators als Einzel- oder Teamprojekt:

• Bearbeitung eines ausgewählten, aktuellen Themas zur Cybersicherheit
• Lösung einer Forschungsaufgabe wie zum Beispiel aus:
  • System-, Netzwerk- und Anwendungssicherheit Kryptologie
  • Protokolle Mediensicherheit und
  • Biometrische Systeme
  • Spezifikation und formale Verifikation sicherer Systeme
  • IT Sicherheitsmanagement

Arbeitsaufwand:

• 28 h Präsenzzeit (Projektbesprechung, Abgabe und Abnahme)
• 152 h selbstständige Arbeit (132 h Entwicklung einer Softwarelösung 20 h Vorbereitung und Durchführung einer Präsentation und der Abgabe der Ergebnisse)

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
keine	Hausarbeit (Beschreibung der erarbeiteten Lösung und der zugehörigen Evaluationsergebnisse)	Wissenschaftliches Projekt (2 SWS)

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

Hinweise:
https://omen.cs.uni-magdeburg.de/itiamsl/deutsch/lehre/index.html

Recent Topics in Business Informatics

every

(in German: Recent Topics in Business Informatics )

Module-ID: FIN-INF-120318

Abbreviation RTBI

Credit Points 6

Semester every

Term

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr. Klaus Turowski
Lecturer:	Dr. Daniel Staegemann , Abdulrahman Nahhas
Classes:	Seminar RTBI
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Human Factors - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Intended learning outcomes:
Students ...

• can acquire new knowledge independently through self-sufficient research and consultation of thematically relevant scientific literature and make it usable for themselves.
• can write a scientific paper almost independently and with only a small amount of support and present the final result.
• can convey knowledge from a subject area of their choice as part of a specialist presentation, explain the research results they have achieved and defend the arguments they have put forward in a scientific discussion.
• know the steps involved in writing a scientific paper, from finding a specific topic to presenting it at a conference.
• have carried out the necessary steps for crafting a scientific publication themselves and have developed an understanding of them.

Content:

• Research, Literature Search, Citation & Plagiarism
• Scientific methods
• Hands-On-Guidance
• Introduction to selected Business Informatics topics

Workload:
presence = 40 hr.:

40 h lecture with integrated exercise

independent work = 140 hr:

Follow-up of the lecture, write paper and review, and present paper.

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Referat	Seminar (4SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Basics of scientific writing

Media:	Literature:

Comments:

Recommenders

Summer

Learning Methods and Evaluation Methods for Recommenders

(in German: Recommenders - Master level )

Module-ID: FIN-INF-120525

Abbreviation RECSYS-M

Credit Points 9

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Myra Spiliopoulou
Lecturer:	Myra Spiliopoulou
Classes:	Lecture Recommenders Exercise class Recommenders
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
When successfully completing this module, the students have acquired soft skills and technical expertise, as follows: SOFT SKILLS: They

• have learned how to critically discuss and valuate a scientific paper

TECHNICAL EXPERTISE: They

• comprehend the business context of recommenders
• comprehend the different types of recommenders, their advantages and disadvantages from the business perspective and from the mining perspective and are familiar with advanced recommendation methods
• have acquired advanced insights on how to critically evaluate a recommender from the mining perspective and the business perspective
• understand why adaption is necessary in recommenders and comprehend the behaviour of temporal methods for recommenders

and have thus acquired skills they need in order to critically appreciate off-the-shelf or state-of-the-art methods and to design workflows that encompass recommender learning, tuning and evaluation

Content:
The course consists of two blocks:

• Block 1 is on Fundamentals of Recommenders and has mutiple parts: (1.1) recommender goals and workflows, (1.2) simple recommendation methods, (1.3) evaluation mechanisms, (1.4) recommender adaption on a stream
• Block 2 covers advanced topics of recommenders: students read and critically discuss papers in class

Workload:

• 28 Std/hours: Vorlesung/Lecture - in presence (lecture class: 2 hours per week, 28 hours)
• 28 Std/hours: Übung/Exercises - in presence (exercise class: 2 hours per week, 28 hours)
• 214 Stunden: Individual working time on
  • Preparation for lectures and exercise classes
  • Reading and understanding the articles provided
  • Discussion of the articles in class

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
A minimum number of points must be achieved during the classes	Schriftliche Prüfung (Klausur)	Vorlesung (2 SWS) - Lecture class (2 hours-per-week) Übung (2 SWS) - Exercise class (2 hours-per-week)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Data Mining I Machine Learning Data Mining II Introduction to Deep Learning Advanced English skills Reading of scientific papers, as acquired eg in a bachelor seminar

Media:

Literature:

Literature for Block 1: selected chapters from the 'Recommender Systems Handbook' in the editions: Recommender Systems Handbook by Francesco Ricci, Lior Rokach and Bracha Shapira (editors), SPRINGER (2015), 2nd edition, https://doi.org/10.1007/978-1-4899-7637-6 Recommender Systems Handbook by Francesco Ricci, Lior Rokach and Bracha Shapira (editors), SPRINGER (2022), 3rd edition, https://doi.org/10.1007/978-1-0716-2197-4 Further cited literature, including scientific articles and use cases are cited at the beginning of each unit. Literature for Block 2: the papers for the discussions in class will be announced during the course

Comments:

Scrum-in-Practice

Winter

(engl. Scrum-in-Practice )

Modulnummer: FIN-INF-120438

Kürzel SIP

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Prof. Dr. Frank Ortmeier
Dozent:in:	Prof. Dr. Frank Ortmeier
Lehrveranstaltungen:	Vorlesung Scrum-in-Practice Übung Scrum-in-Practice Die Lehrveranstaltungen finden zum Teil im Blockformat statt.
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen

Angestrebte Lernergebnisse:
Die Studierenden ...

• kennen die Projektmanagementmethode Scrum
• wenden agile Softwareentwicklungsmethoden praktisch an
• gewinnen praktische Erfahrungen mittels Durchführung eines Projektes
• reflektieren ihr Selbst- und Projektmanagement

Inhalt:

• In diesem Modul werden theoretische Kenntnisse und praktische Kompetenzen im Umgang mit Scrum vermittelt.
• Die Veranstaltung besteht aus drei Teilen.
• In einem Einführungsteil werden in zwei Vorlesungen die notwendigen Konzepte des Scrum-ProzessModells vorgestellt und die zur erfolgreichen Projektdurchführung notwendigen Technologien aufgelistet.
• Im Hauptteil der Veranstaltung wird in einer 1-wöchigen Blockveranstaltung ein Projekt mittels Scrum umgesetzt. Diese erfolgt in Projektteams von 4-5 Teilnehmern.
• Während dieser Phase finden 2mal täglich Scrum-Meetings mit den Betreuern statt. Als Ergebnis lernen die Teilnehmer zielgerichtet und effizient nach diesem Entwicklungsmodell zu entwickeln.
• Die Blockveranstaltung findet in einer Woche während der Vorlesungsfreien Zeit. Hier besteht natürlich Anwesenheitspflicht.
• Zur erfolgreichen Durchführung in der Projektarbeit wird von jedem Teilnehmer ein sorgfältiges Einarbeiten in die notwendigen Technologien erwartet.
• Nach Abschluss der Projektwoche reflektieren die Teilnehmer Ihre Erfahrungen und fassen diese zusammen. Diese Ergebnisse werden dann in einer gemeinsamen Abschlussveranstaltung diskutiert.

Arbeitsaufwand:
180h = 56h Präsenszeit + 124h selbständige Arbeit am Projekt

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
Projekt + Ausarbeitung	mündliche Prürfung	Blockseminar / Projektarbeit

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine	Software Engineering

Medienformen:	Literatur:

Hinweise:
Zuangsbeschränkung: Limitierte Teilnehmerzahl. Auswahlverfahren wird in erster Vorlesung bekannt gegeben

Selected Chapters of IT Security: Orchestration of Mechanisms and Tools

Winter

(in German: Selected Chapters of IT Security: Orchestration of Mechanisms and Tools )

Module-ID: FIN-INF-120505

Abbreviation ITSEC-Orch

Credit Points 6

Semester Winter

Term ab 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr.-Ing. Jana Dittmann
Lecturer:	Prof. Dr.-Ing. Jana Dittmann
Classes:	Projekt Selected Chapters of IT-Security
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Informatik Wahlpflicht (SPO 2027) - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen - M.Sc. VC: Computer Science (SPO 2027) - M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027)

Intended learning outcomes:
Die Studierenden ...

• kennen und verstehen Aspekte der Orchestrierung
• können ausgewählte Forschungsfragen der Orchestrierung selbständig bewältigen
• können bestehende Ansätze bewerten und kreativ für eine Forschungsfrage erweitern
• können sich in den semesterspezifischen Anwendungsfall einarbeiten und im Team reflektieren
• können für die Fragestellung neue Ansätze zur Orchestrierung aufzeigen
• können für den Semester-spezifischen Anwendungsfall Sicherheitsbedarfe feststellen und Verbesserungsalternativen vorschlagen
• können eine ausgewählte Lösung umsetzen, evaluieren und bewerten sowie Restrisiken aufzeigen
• können selbständig wissenschaftliche Methoden der IT-Sicherheit anwenden
• können die Ergebnisse wissenschaftlich in einem Referat präsentieren und sich einer Diskussion stellen
• können Herausforderungen in der Teamarbeit erkennen und reflektieren sowie angehen

Content:
Inhalte zu aktuellen Herausforderungen und Lösungen der IT Sicherheit mit ausgewählten Fragen der Orchestrierung als technisch-organisatorische Gestaltungsaufgabe wie zum Beispiel aus:

• Sicherheitsmanagement Standardisierung,
• Zertifizierung und Evaluation Technische Gestaltung vor Rechtlichen Erwartungshorizonten
• ethische und sozial Aspekte der IT-Sicherheit einschließlich Nachhaltigkeit, digitale Inklusion und Barrierefreiheit
• Sicherheit im E-Business
• Fallstudien zur IT-Sicherheit

Workload:
28 h Präsenzzeit 152 h selbstständige Arbeit

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
keine	Hausarbeit	Forschungsprojekt (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:
https://omen.cs.uni-magdeburg.de/itiamsl/deutsch/lehre/index.html

Seminar 'Advanced Topics of KMD'

every

(in German: Seminar 'Advanced Topics of KMD' )

Module-ID: FIN-INF-120330

Abbreviation AdvKMD_Seminar

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Myra Spiliopoulou
Lecturer:	Myra Spiliopoulou
Classes:	Seminar AdvKMD_Seminar
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DKE: Data Processing for Data Science - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
When successfully completing this seminar, the students:

• know how to collect literature on a specific topic, and can judge which literature is relevant and which is not
• can compare mining methods, workflows and frameworks that address a specific scientific topic, and can rank them on given criteria
• can formulate such criteria themselves
• can critically discuss the advantages and caveats of a published mining method
• can summarize the papers they collected, their comparisons and critical discussion in an essay
• can present the papers they collected, their comparisons and critical discussion in a pre-specified amount of time and in a way understandable to both an audience that has read the papers and to an audience that has not read, and they can answer questions

Content:
CONTENT: Topics on 'Advanced Knowledge Management and Discovery' for business applications, engineering applications and applications in healthcare; all topics refer to mining algorithms for static or dynamic data STRUCTURE: Each student receives a topic as assignment. The assignment encompasses:

• one or more 'seed' papers in the topic's area
• a series of questions to be answered, and which concern state of the art (SOTA) methods in the topic area, challenges addressed by these methods, evaluation criteria on the performance of these methods, evaluation procedures and datasets

The student must:

1. collect additional literature from the topic area in a transparent manner
2. compare the scientific advances in the collected literature in order to answer the scientific questions
3. critically discuss and rank the methods compared in the previous step
4. present the collected literature, the comparisons and the results in class
5. write an essay on them

Workload:

• 28 hours Seminar+Consultations in class
• 124 hours self-study work on scientific papers

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Referat	Seminar (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Familiarity with advanced mining methods Familiarity with procedures for the evaluation of models and the comparison of models Underpinnings of scientific writing (e.g. through a bachelor seminar or a bachelor thesis)

Media:	Literature:
	The literature of the seminar covers several topics. For each topic, it consists of the papers assigned to each student and the paper that the student collects on that topic. The topics change in each semester.

Comments:
The seminar contains several topics. The mapping of the module (see field 'Verwendbarkeit') depends on the topic assigned to a specific student.

Seminar on Advanced Topics of Predictive Maintenance

every

(in German: Seminar on Advanced Topics of Predictive Maintenance )

Module-ID: FIN-INF-120489

Abbreviation PredMa_Masterseminar

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr.-Ing. Benjamin Noack, Prof. Dr. Myra Spiliopoulou
Lecturer:	Prof. Dr.-Ing. Benjamin Noack, Prof. Dr. Myra Spiliopoulou
Classes:	Seminar Advanced Topics of Predictive Maintenance
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. WIF: Wirtschaftsinformatik (SPO 2027) - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Digital Engineering (SPO 2027) - M.Sc. DE: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. VC: Computer Science - M.Sc. VC: Computer Science (SPO 2027)

Intended learning outcomes:
Students who complete the course ...

• read scientific articles given to them on introductory topics of predictive maintenance
• discuss these articles and reflect on them
• compare the methods that appear in these articles and discuss the pros and contra of each method in comparison to other methods
• summarize article contents on predictive maintenance in a report and present them
• formulate questions on these articles and answer questions formulated by other seminar participants

Content:
In this seminar, the participants will learn about

• challenges and methods for data acquisition in industrial processes
• data analysis tools in predictive maintenance
• process modeling, fault detection, and state prediction for industrial applications

Workload:
28h in presence (consultation meetings, presentations) + 156 h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Referat	Seminar (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Signal and information processing Learning algorithms Underpinnings of engineering sciences and/or business informatics (some topics only) Principles of scientific writing

Media:	Literature:
	Literature depends on topic assignment and is given to each student together with the assignment

Comments:

Software Defined Networking

Summer

(in German: Software Defined Networking )

Module-ID: FIN-INF-120460

Abbreviation SDN

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	David Hausheer
Lecturer:	David Hausheer
Classes:	Lecture Software Defined Networking Exercise Software Defined Networking
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Intended learning outcomes:
Students...

• gain competence in the area of software defined networking
• understand software defined networking concepts
• can explain these concepts to other participants
• are able to apply these concepts in theoretical use cases and practical exercises
• gain expertise in practical network applications

Content:
The course deals with topics in the area of software defined networking:

• SDN Architecture (Application, Control, Infrastructure Layer)
• SDN Interfaces (North/South-bound vs. East/West-bound interface)
• SDN Applications and Use Cases (e.g. Multicasting)
• Network Virtualization and Slicing (e.g. FlowVisor)
• Network Function Virtualization (NFV) and Network Service Chaining
• SDN Security
• Network Operating Systems and Languages
• OpenFlow Controller (e.g. NOX, Beacon, etc.)
• Hardware Switches (e.g. NEC IP8800, Pronto) vs. Software Switches (e.g. NetFPGA, OpenVSwitch)
• SDN in Wireless Networks (e.g. OpenWRT)

Workload:

• 28h lectures
• 22h theoretical exercises
• 6h practical exercises
• 124h homework (further studies, practical work, preparation for the final exam)

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Prerequisites for the exam required; details will be announced at the beginning of the semester.	Written exam 120 minutes	2 SWS lecture 2 SWS exercise

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Modul Computernetze

Media:	Literature:
	Textbooks as indicated. Slides and paper copies as necessary.

Comments:

Software-Development for Industrial Robotics

Winter

(engl. Software-Development for Industrial Robotics )

Modulnummer: FIN-INF-120432

Kürzel SDIR

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Prof. Dr. Frank Ortmeier
Dozent:in:	Prof. Dr. Frank Ortmeier
Lehrveranstaltungen:	Vorlesung Software-Development for Industrial Robotics Übung Software-Development for Industrial Robotics
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Angestrebte Lernergebnisse:
The students have ...

• Understanding of problems in the robotics domain
• Understanding and applicability of the mathematical background
• Practical experience in programming industrial robots on the basis of various tasks

Inhalt:
The use of industrial robots is increasing rapidly these days. In 2014, the expected number of industrial robots increased by 27% compared to the previous year. The main reason is their flexibility, especially their ability to perform a wide range of tasks. In the lecture “Software Development for Industrial Robotics” an overview of this domain is given as well as the mathematical background. The latter deals in particular with the idea of forward and inverse kinematics, point-to-point movements, linear movements, trajectory planning, recognition of singularities, Denavit-Hartenberg convention, rotation and translation matrices. The final project deals with control using a collision-free path planner, KUKA youBot kinematics, numerical approaches to solving inverse kinematics, etc.

Arbeitsaufwand:
180h = 56h attendance time + 124h independent work

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
Projekt	mündliche Prüfung	Lecture (2 SWS) Exercise (2 SWS)

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
none

Medienformen:	Literatur:

Hinweise:
Zuangsbeschränkung: Limitierte Teilnehmerzahl. Auswahlverfahren wird in erster Vorlesung bekannt gegeben

Swarm Intelligence

Winter

(in German: Swarm Intelligence )

Module-ID: FIN-INF-102419

Abbreviation SI

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Sanaz Mostaghim
Lecturer:	Sanaz Mostaghim
Classes:	Vorlesung Swarm Intelligence Übung Swarm Intelligence
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Intended learning outcomes:
The students will get an understanding about Swarm Intelligence and its definitions. They will get the ability to develop swarm intelligence algorithms such as swarm aggregation, swarm movements, tracking, tracing and optimization. In addition, they learn the foundations in collective decision-making and their applications. The students can implement, analyse and evaluate swarm algorithms on several applications.

Content:
This course provides in-depth knowledge about swarm intelligence in technical systems. In swarm intelligence, we deal with a group of simple and usually homogenous individuals with simple rules. Usually, swarms can achieve a complex and intelligent behaviour using local interactions between its members. This collective property can be used in technical systems. One advanced application of swarm intelligence is in swarm robotics, in which simple small robots can collectively learn to achieve some predefined complex tasks. During this course, the algorithms of swarm intelligence are presented, analysed and compared. The following topics will be covered: Part 1: Fundamentals of swarm intelligence

• Swarm stability and stability analysis
• Swarm aggregation
• Swarm in known environments
• Swarm in unknown environments: Particle Swarm Optimization
• Dynamic Optimization
• Multi-Objective Particle Swarm Optimization

Part 2: Swarm and multiagent systems

• Division of labour and task allocation
• Swarm clustering and sorting
• Ant systems and optimization

Part 3: Applications

• Swarm localization and display
• Swarm robotics

Workload:
Präsenzzeit: 2 SWS Vorlesung 2 SWS Übungen Selbstständige Arbeit: Bearbeiten von Übungs- und Programmieraufgaben 180 h = 56 h Präsenzzeit + 124 h selbstständige Arbeit

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
All students that participate can take the exam.	Written Exam (120 min)	Vorlesung (2 SWS) Übung (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Informatik (Algorithmen und Datenstrukturen, Maschinelles Lernen)

Media:

Literature:

Eric Bonabeau, Marco Dorigo and Guy Theraulaz, Swarm In-telligence: From Natural to Artificial Systems, Oxford University Press, 1999Andries Engelbrecht, Fundamentals of Computational Swarm Intelligence, Wiley 2006 James Kennedy and Russel Eberhart, Swarm Intelligence, Morgan Kaufmann, 2001 Zbigniew Michalewicz and David Fogel, How to solve it: Modern Heuristics, Springer, 2001 Veysel Gazi, Stability Analysis of Swarms, The Ohio State University, 2002 Marco Dorigo and Thomas Stützle, Ant Colony Optimization, The MIT Press, 2004 C. Solnon: Ant Colony Optimization and Constraint Program-ming. Wiley 2010 Gerhard Weiss, Multiagent Systems: A modern approach to distributed artificial systems, The MIT Press, 2000 Christian Müller-Schloer, Hartmut Schmeck and Theo Ungerer, Organic Computing – A Paradigm Shift for Complex Systems, Springer, 2011

Comments:
Enrollment is limited to a maximum number of participants. If necessary, students will be selected based on a statement of purpose submitted at the beginning of the semester.

VLBA - Systemarchitekturen

Sommer

(engl. VLBA: Systemarchitectures )

Modulnummer: FIN-INF-120252

Kürzel VLBA-SysA

CP 9

Semester Sommer

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Prof. Dr. Klaus Turowski
Dozent:in:	Prof. Dr. Klaus Turowski
Lehrveranstaltungen:	Vorlesung VLBA: Systemarchitekturen Übung VLBA: Systemarchitekturen
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Angestrebte Lernergebnisse:
Die Studierenden ...

• sammeln Know-how über die Architektur von Systemlandschaften.
• wissen wie Geschäftsprozesse und IT-Ressourcen miteinander verbunden werden können (Wie spiegelt sich dies in der (System-)Architektur wider).
• kennen die Unterschiede zwischen monolithischen und serviceorientierten Architekturen bei der Entwicklung und Integration von Anwendungssystemen für Unternehmen.
• wissen, wie man Anwendungskomponenten zur Unterstützung verschiedener Geschäftsprozesse ableitet.
• wissen wie Komponenten und Dienste als Elemente einer Systemarchitektur systematisch identifiziert werden.
• wissen wie Cloud Computing-Technologien zur Unterstützung von Geschäftsprozessen genutzt werden können.
• kennen die Unterschiede zwischen den verschiedenen Cloud- Patterns und den Cloud-Service-Modellen.
• lernen die Cloud Application Architecture kennen und erfahren, wie die Microservice-Architektur die klassische serviceorientierte Architektur in Cloud-Umgebungen ergänzt.
• können sich selbstständig neues Wissen aneignen und für sich nutzbar machen.
• können bei ungewohnten Aufgabenstellungen eigene Ideen einbringen und Entscheidungen treffen.
• können für neue Aufgabenstellungen geeignete Lösungen auswählen, anwenden und anschließend bewerten.
• können die Ergebnisse wissenschaftlich in einem Referat präsentieren und sich einer Diskussion stellen.

Inhalt:

• Theorie der komponentenbasierten Systementwicklung
• Fachkomponenten,
• Frameworks,
• Komponenten-Lebenszyklen,
• CoBCoM-Architektur
• Architekturen von Systemlandschaften
• Pattern-Sprachen und Architektur-Pattern
• Service-orientierte Architektur (SoA)
• Web-Services
• Mediatoren
• Fallstudien
• Personal Information
• GuideShared ERP Architecture
• Inhaltsverzeichnis Teil A (Winter) Seite 250 – Teil A Inhaltsverzeichnis Teil B (Gesamt)
• S4/Hana und SAP Datasphere
• Prototypische Realisierung eines interorganisationalen Informationssystems auf Grundlage der CoBCoM-Architektur und SoA

Arbeitsaufwand:
Präsenzzeiten:

28 h Vorlesung / 28 h Übung

Selbstständiges Arbeiten:

1. Vor- und Nachbereitung Vorlesung
2. Entwicklung einer auf maschinellem Lernen basierenden Lösung unter Anwendung von MLOps-Praktiken zur Lösung betriebswirtschaftlicher Probleme 

9 x30h (56 h Präsenzzeit + 214 h selbstständige Arbeit)

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
	Referat	Vorlesung (2 SWS) Übung (2 SWS)

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
Version control systems, DevOps practicies (CI/CD), Containerization technologies, and python.	Version control systems, DevOps practicies (CI/CD), Containerization technologies, and python.

Medienformen:	Literatur:
	Turowski, K.: Fachkomponenten. Aachen 2002. Herden, S., Marx Gómez, J., Rautenstrauch, C., Zwanziger, A.: Softwarearchitekturen für E-Business-Systeme, Berlin, Heidelberg u. a., 2006.

Hinweise:

VLBA – Cloud DevOps Technologies

Winter

(in German: VLBA – Cloud DevOps Technologies )

Module-ID: FIN-INF-120478

Abbreviation VLBA-CDOT

Credit Points 9

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr. Klaus Turowski
Lecturer:	Prof. Dr. Klaus Turowski
Classes:	'- Lecture VLBA Cloud DevOps Technologies Exercise VLBA Cloud DevOps Technologies Consultation VLBA - Cloud DevOps Technologien (Consultation) Tutorial VLBA - Cloud DevOps Technologien (Tutorial)
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. DKE: Data Processing for Data Science - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
Students ...

• know and understand specialized subject content that expands and deepens the Bachelor's degree.
• can independently analyze problems and identify alternative solutions. -know how to work with UNIX operating systems.
• are experienced in the theory and handling of container-based virtualization technologies such as Docker.
• know how to use higher programming languages (Python).
• know how to use various configuration and automation tools and languages such as Ansible, Salt and Terraform.
• have practical and theoretical knowledge of testing IT systems, particularly in relation to essential DevOps concepts such as Continuous Integration and Continuous Delivery.
• have the ability to design, create, test, commission and support IT systems.

Content:
In view of highly networked applications, big data and cloud computing, the careful planning and construction of architectures and landscapes of the corresponding information systems is more important than ever. In many cases, the lifecycle of a system does not end with the delivery of the solution, but rather the subsequent operation, monitoring and maintenance have become an essential part of this process. At this point, sophisticated paradigms and methods are required to facilitate the continuous development and operation of these systems while preventing errors, failures and other disruptions. The course is designed to teach the fundamentals as well as initial practical experience in the development and Table of Contents Part A (Winter) Page 248 - Part A Table of Contents Part B (Full) operation (DevOps) of systems in conjunction with dedicated cloud technologies. In addition to the theoretical basics, essential concepts and technologies are discussed and applied that enable the continuous integration, delivery and testing of corresponding systems.

Workload:
Attendance = 84 h: 21 h Lecture 21 h Exercise 21 h Consultation 21 h Tutorial Independend work = 186 h: 186 h working on several assignments that build on each other

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
In order to qualify for the final project, certain requirements must be fulfilled. Details will be announced in the first lecture.	Hausarbeit	Vorlesung (2 SWS) Übung (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:

Wissenschaftliches Individualprojekt

jedes

(engl. Individual Scientific Project )

Modulnummer: FIN-INF-120301

Kürzel WIP

CP 6

Semester jedes

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Studiendekan:in der FIN
Dozent:in:	Alle Dozenten der FIN
Lehrveranstaltungen:	keine
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INF: Projekte (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. WIF: Projekte (SPO 2027) - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DKE: Data Processing for Data Science - M.Sc. DKE: Applied Data Science - M.Sc. DKE: Projects (SPO 2027) - M.Sc. DE: Human Factors - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. DE: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DE: Projekte (SPO 2027) - M.Sc. VC: Visual Computing - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. VC: Projekte (SPO 2027)

Angestrebte Lernergebnisse:
Die Studierenden ...

• arbeiten selbstständig an einer wissenschaftlichen Fragestellung
• arbeiten sich selbstständig in ein wissenschaftliches Spezialgebiet ein
• erarbeiten vertiefte Kenntnisse auf einem wissenschaftlichen Spezialgebiet
• präsentieren und interpretieren ihre Ergebnisse in mündlicher und/oder schriftlicher Form
• können ihren Erfolg und die Bedeutung ihrer Ergebnisse einschätzen
• beschreiben die Auswirkungen ihrer Entscheidungen auf den Verlauf und die Ergebnisse des Projektes

Inhalt:
Die Studierenden bearbeiten in selbstständiger Arbeit eine individuell vereinbarte, wissenschaftliche Fragestellung unter Anleitung eines Hochschullehrers. Im Projekt entwickeln die Studierenden eine eigenständige Informatik-Lösung. Dadurch erwerben sie ein detailliertes und kritisches Verständnis in einem Spezialbereich der Informatik. Dieses Modul wird von unterschiedlichen Hochschullehrern implementiert. Die fachlichen Inhalte sind daher angebotsspezifisch.

Arbeitsaufwand:
180h selbstständige Arbeit

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
	Wissenschaftliches Projekt	Angeleitetes wissenschaftliches Individualprojekt

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

Hinweise:
Projekte außerhalb der FIN müssen von Studiengangsleiter abgesegnet werden.

Scientific Teamproject KMD

every

(in German: Wissenschaftliches Teamprojekt KMD )

Module-ID: FIN-INF-120528

Abbreviation Teamproj_KMD

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Myra Spiliopoulou
Lecturer:	Myra Spiliopoulou
Classes:	Teamproj_KMD
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Interdisziplinäres Team-Projekt - M.Sc. DE: Fachliche Spezialisierung - M.Sc. DE: Digital Engineering-Projekt - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen

Intended learning outcomes:
When completing this module successfully, the students can: * solve a realistic data mining and data engineering task in teamwork * assess the business implications (costs and benefits) of a solution they propose * built-up a team and organize themselves in it, distributing subtasks among themselves according to their competences, setting milestones and pursuing a joint schedule * search for scientific papers of relevance * search appropriate libraries for data preparation, learning and visualization * design a learning workflow, apply AI methods and evaluate the induced models * develop a software solution jointly, using public domain tools * justify their design decisions and software suite selections * present their work as a team * write a joint report where they summarize and justify their approachBitte nachtragen nach neuen Constructive Alignment Vorgaben.

Content:
CONTENT: Assignment in the form of a realistic task that involves the analysis of static or dynamic, structured or unstructured data with mining methods, including supervised, unsupervised and semi-supervised methods, stream miners, forecasters and elaborate AI tools. The assignment involves design, development and evaluation of a software solution in teamwork. STRUCTURE: The assignment is for a team of students, typically three; for larger teams, the assignment is extended to ensure that the effort of each student is 6 ECTS. The students are called to solve the task as a team. The task involves understanding a realistic problem, designing, developing and evaluating a solution for it, presenting this solution to the class, defending it and documenting it on paper.

Workload:
For each team member: 28 h in class (including meetings) + 124 h self-study The teamproject is typically for three students. For larger teams, the assignments is extended to ensure that the effort of each team member is as above.

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Wissenschaftliches Projekt, implementiert als eine Hausarbeit, die ein Referat beinhaltet COURTESY TRANSLATION: This module implements the exam form 'Wissenschaftliches Projekt' as a 'Hausarbeit' which incorporates a 'Referat'.	Wissenschaftliches Teamprojekt (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Familiarity with advanced mining methods for static data and (for some assignments) for streams Familiarity with software development and software libraries Underpinnings of project management

Media:	Literature:
	Literature depends on topic assignment and is given to each team together with the assignment

Comments:
Als Implementierung des generischen PFLICHTMODULS "Wissenschaftliches Teamprojekt" entsprechend anrechenbar. COURTESY TRANSLATION: This module implements the COMPULSORY generic module 'Wissenschaftliches Teamprojekt'.

Modules for Schlüssel- und Methodenkompetenzen (Soft Skills)

AMS Lab Projects

every

(in German: AMS Lab Projects )

Module-ID: FIN-INF-120503

Abbreviation AMS-Lab

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr.-Ing. Benjamin Noack
Lecturer:	Prof. Dr.-Ing. Benjamin Noack
Classes:	AMS Lab Projects
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INF: Projekte (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. WIF: Projekte (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DKE: Projects (SPO 2027) - M.Sc. DE: Interdisziplinäres Team-Projekt - M.Sc. DE: Fachliche Spezialisierung - M.Sc. DE: Digital Engineering-Projekt - M.Sc. DE: Digital Engineering (SPO 2027) - M.Sc. DE: Engineering Specialization (SPO 2027) - M.Sc. DE: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DE: Projekte (SPO 2027) - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen - M.Sc. VC: Computer Science (SPO 2027) - M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027)

Intended learning outcomes:
Students who complete the course ...

• can collaborate in teams to solve challenges in technical domains and deepen their technical expertise.
• can manage a project and define goals and milestones
• can acquire the knowledge in a technical domain to derive solutions
• can present and document their work and achievements

Content:
This module offers different team projects and individual projects on current, research-related areas like:

• robotics
• sensor data processing
• navigation and planning
• industrial machine learning
• optimization and control
• and more

Workload:
180 h of individual and team work

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Scientific Project (Wissenschaftliches Projekt)	Project (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:
	Literature will be announced in the lecture.

Comments:

Clean Code Development

Winter

(in German: Clean Code Development )

Module-ID: FIN-INF-120432

Abbreviation CCD

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr. Frank Ortmeier
Lecturer:	Prof. Dr. Frank Ortmeier
Classes:	Vorlesung Clean Code Development Übung Clean Code Development
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen

Intended learning outcomes:
Students:

• know and understand the principles of Clean Code Development
• are able to apply the associated tools and practices in practice
• learn to independently select and adapt the concepts suitable for a project
• learn to distribute tasks within a team and collaborate in an organized manner

Content:

• Software engineering deals with the creation and development of software, the organization and modeling of related data structures, and the operation of software systems. It thus encompasses software design, implementation, and management.
• The introductory lecture Software Engineering I provided the foundational knowledge for designing and writing good software.
• This advanced lecture introduces modern techniques and methods that are commonly used in the development of large software systems.
• We align with the four core values of Clean Code Development – evolvability, correctness, production efficiency, and comprehensibility.
• To achieve these goals, a variety of programming principles and practices will be introduced.
• These include, among others, team formation and organization in software development, principles and tools of Clean Code Development, continuous integration and automated build systems, bug tracking, error localization and debugging, automated and model-based testing, code analysis and quality metrics, requirements engineering and tracing, as well as distributed and component-based software architectures.
• The course will offer a high degree of practical engagement with tools and software development techniques, especially in the exercises. The goal is not only to convey abstract knowledge but also to develop hands-on competencies.
• Participation in the exercises is mandatory and will be conducted in groups.

Workload:
56h Präsenszeit + 124h selbständige Arbeit am Projekt

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
Projekt	Mündliche Prüfung	Vorlesung (2 SWS) Übung (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Software Engineering

Media:	Literature:

Comments:
Enrollment Restriction: Limited number of participants. The selection process will be announced during the first lecture.

Data Science with Python

Winter

(in German: Data Science with Python )

Module-ID: FIN-INF-120513

Abbreviation DSWP

Credit Points 6

Semester Winter

Term

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Dr. Christian Beyer
Lecturer:	Dr. Christian Beyer
Classes:	Data Science with Python
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Interdisziplinäres Team-Projekt - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen

Intended learning outcomes:
The course is about learning from data to perform predictions and obtain useful insights. In the seminar, we will use the programming language Python.
Necessary skills to manage and analyze data will be taught and practiced on real-world applications. Programming knowledge from other courses is helpful but not mandatory. However, students are expected to have a profound knowledge of fundamental data-analysis techniques, such as classification, regression and clustering. After successful completion of this course, the student will be able to perform the following tasks in Python:

• Import and preprocess raw data (files, databases, web APIs)
• Transform data for modelling
• Perform exploratory data analysis with summary statistics and visualization
• Understand, build and evaluate predictive classification and regression models, including tree-based models, ensembles and boosted models
• Communicate and disseminate results and findings through reproducible documents, presentations, websites and interactive web applications

Content:
Part Fundamentals & Visualization: Basics, scripts, workflows, vectors & functions in Python Explorative data visualization Data transformation Part Data Management & Exploratory Data Analysis: Data cleaning & scraping Generating hypotheses and an intuition about the data with exploratory data analysis Data import Data management Relational data Strings, categorical data, dates & time Iteration: imperative & functional programming Part Modeling: Linear regression Classification Evaluation Model selection & regularization (LASSO, Ridge) Feature selection & model interpretation Decision trees Ensembles: random forests Boosting: gradient boosted trees Unsupervised learning, e.g. k-means, hierarchical clustering, self-organizing maps, principal component analysis Topic modeling with simple graphical models Statistical testing Part Communication: Communication and dissemination of results through visualization and interpretable summaries with documents, notebooks, presentations & websites

Workload:
Attendance time = 28 h: - 2 SWS weekly seminar; Independent work outside the actual Seminar time = 152 h: - 76 h preparation and follow-up of the seminar topics - 76 h solving the tasks, incl. work in the laboratory 180h = 28h attendance time + 152h independent work

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Referat	Seminar (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Area 1: Data Mining, Machine Learning, Artificial Intelligence Area 2: Databases Area 3: Programming Languages and Software Engineering Area 4: Stochastics, Applied Statistics

Media:	Literature:
	Will provided during the seminar

Comments:

Data and Knowledge Engineering

Sommer

(in German: Data and Knowledge Engineering )

Module-ID: FIN-INF-120500

Abbreviation

Credit Points 6

Semester Sommer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Andreas Nürnberger
Lecturer:	Andreas Nürnberger
Classes:	Seminar Data and Knowledge Engineering
Applicability in curriculum:	- M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Fundamentals of Data Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen

Intended learning outcomes:
Students who complete the course ...

• understand how to work in practice with DKE approaches
• can perform a literature review on the state-of-the-art on a given topic
• can describe a given DKE task in form of a scientific paper
• can present and defend a practical project by a presentation and discussion

Content:
The seminar focuses on applying machine learning techniques to a hands-on problem, resulting in a paper and short presentation in one of the following research areas: Information Retrieval:

• Document organization
• Information management
• Ranking & Explainability

Natural Language Processing

• Named Entity Recognition
• Tokenization & Filtering
• Latent Representations of Words / Sentences / Documents

Human Computer Interaction

• Usability
• User Experience (UX) Design
• Human-AI interaction

Workload:
180 h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Written report and oral presentation (Referat). Requirements for exam participation will be announced in the first week of the course in class and online.	Seminar

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:

Design-Projekt

Sommer

(engl. Design Project )

Modulnummer: FIN-INF-120520

Kürzel DSP

CP 6

Semester Sommer

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Hans-Knud Arndt
Dozent:in:	Hans-Knud Arndt
Lehrveranstaltungen:	Seminar Design Projekt Übung Design Projekt
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Human Factors - M.Sc. VC: Schlüssel- und Methodenkompetenzen

Angestrebte Lernergebnisse:
Absolvent*innen dieses Moduls...

• entwickeln ein Verständnis für Industrie-Design
• setzen sich mit den „Zehn Thesen des Guten Designs“ von Dieter Rams auseinander und sind in der Lage diese auf Projekte anzuwenden
• entwickeln ein Verständnis für das Design aktueller IT-Produkte
• sind in der Lage, das Design von aktuellen IT-Produkte zu entwickeln und zu bewerten

Inhalt:

• Die Teilnehmer*innen sollen im Rahmen dieser Lehrveranstaltung ihre neu gewonnenen Erkenntnisse in Bezug auf Design praktisch auf IT-Produkte anwenden. Hierbei werden die von Dieter Rams entwickelten „Zehn Thesen des Guten Designs“ eingesetzt.
• Darüber hinaus werden weitere Ansätze, wie zum Beispiel von John Maeda: "Simplicity: Die zehn Gesetze der Einfachheit" angewendet.

Arbeitsaufwand:
Präsenzzeiten:

• 2 SWS Seminar = 28h
• 2 SWS Übung = 28h

Selbstständiges Arbeiten:

• Vor- und Nachbereitung Vorlesung Entwicklung von Lösungen für die Übung 124h

180h = 4 SWS = 56h Präsenzzeit + 124h selbstständige Arbeit

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
	Hausarbeit	Seminar (2 SWS) Übung (2 SWS)

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

Hinweise:

Scientific Writing

Winter

(in German: Scientific Writing )

Module-ID: FIN-INF-120511

Abbreviation SciWri

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	PD Dr. Claudia Krull
Lecturer:	Temitope Ibidunni Akinloye
Classes:	Seminar Scientific Writing
Applicability in curriculum:	- M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DE: Human Factors - M.Sc. DE: Fachliche Spezialisierung - M.Sc. DE: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. VC: Schlüssel- und Methodenkompetenzen - M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027)

Intended learning outcomes:

• Students are able to conduct a literature research in order to gain an overview of an area of interest.
• Students can identify open research questions based on a literature research.
• Students are able to identify and formulate research hypothesis
• Students understand how a scientific publication is structured, can read it and extract the important information.
• Students are able to write a research proposal on a selected topic in the form of a scientific publication.
• Students are able to identify and select possible solution methods for a scientific question.
• Students are able to prepare a presentation to explain and defend their research proposal.
• Students understand how to present a research topic proposal on a selected topic.
• Students are able to evaluate the quality of others scientific work

Content:

• Literature citation and paraphrasing
• Presentations Review scientific articles
• Argument formation
• Knowledge and application of academic writing styles
• Peer review assessment

Workload:

• 28h Seminar
• 152h independent work

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
participation in all classes completion of assignments keeping all deadlines	Seminar paper (Paper + Reviews) Presentation	Seminar (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:
participation is limited to 40

Scrum-in-Practice

Winter

(engl. Scrum-in-Practice )

Modulnummer: FIN-INF-120438

Kürzel SIP

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Prof. Dr. Frank Ortmeier
Dozent:in:	Prof. Dr. Frank Ortmeier
Lehrveranstaltungen:	Vorlesung Scrum-in-Practice Übung Scrum-in-Practice Die Lehrveranstaltungen finden zum Teil im Blockformat statt.
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen

Angestrebte Lernergebnisse:
Die Studierenden ...

• kennen die Projektmanagementmethode Scrum
• wenden agile Softwareentwicklungsmethoden praktisch an
• gewinnen praktische Erfahrungen mittels Durchführung eines Projektes
• reflektieren ihr Selbst- und Projektmanagement

Inhalt:

• In diesem Modul werden theoretische Kenntnisse und praktische Kompetenzen im Umgang mit Scrum vermittelt.
• Die Veranstaltung besteht aus drei Teilen.
• In einem Einführungsteil werden in zwei Vorlesungen die notwendigen Konzepte des Scrum-ProzessModells vorgestellt und die zur erfolgreichen Projektdurchführung notwendigen Technologien aufgelistet.
• Im Hauptteil der Veranstaltung wird in einer 1-wöchigen Blockveranstaltung ein Projekt mittels Scrum umgesetzt. Diese erfolgt in Projektteams von 4-5 Teilnehmern.
• Während dieser Phase finden 2mal täglich Scrum-Meetings mit den Betreuern statt. Als Ergebnis lernen die Teilnehmer zielgerichtet und effizient nach diesem Entwicklungsmodell zu entwickeln.
• Die Blockveranstaltung findet in einer Woche während der Vorlesungsfreien Zeit. Hier besteht natürlich Anwesenheitspflicht.
• Zur erfolgreichen Durchführung in der Projektarbeit wird von jedem Teilnehmer ein sorgfältiges Einarbeiten in die notwendigen Technologien erwartet.
• Nach Abschluss der Projektwoche reflektieren die Teilnehmer Ihre Erfahrungen und fassen diese zusammen. Diese Ergebnisse werden dann in einer gemeinsamen Abschlussveranstaltung diskutiert.

Arbeitsaufwand:
180h = 56h Präsenszeit + 124h selbständige Arbeit am Projekt

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
Projekt + Ausarbeitung	mündliche Prürfung	Blockseminar / Projektarbeit

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine	Software Engineering

Medienformen:	Literatur:

Hinweise:
Zuangsbeschränkung: Limitierte Teilnehmerzahl. Auswahlverfahren wird in erster Vorlesung bekannt gegeben

Selected Chapters of IT Security: Orchestration of Mechanisms and Tools

Winter

(in German: Selected Chapters of IT Security: Orchestration of Mechanisms and Tools )

Module-ID: FIN-INF-120505

Abbreviation ITSEC-Orch

Credit Points 6

Semester Winter

Term ab 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr.-Ing. Jana Dittmann
Lecturer:	Prof. Dr.-Ing. Jana Dittmann
Classes:	Projekt Selected Chapters of IT-Security
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Informatik Wahlpflicht (SPO 2027) - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen - M.Sc. VC: Computer Science (SPO 2027) - M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027)

Intended learning outcomes:
Die Studierenden ...

• kennen und verstehen Aspekte der Orchestrierung
• können ausgewählte Forschungsfragen der Orchestrierung selbständig bewältigen
• können bestehende Ansätze bewerten und kreativ für eine Forschungsfrage erweitern
• können sich in den semesterspezifischen Anwendungsfall einarbeiten und im Team reflektieren
• können für die Fragestellung neue Ansätze zur Orchestrierung aufzeigen
• können für den Semester-spezifischen Anwendungsfall Sicherheitsbedarfe feststellen und Verbesserungsalternativen vorschlagen
• können eine ausgewählte Lösung umsetzen, evaluieren und bewerten sowie Restrisiken aufzeigen
• können selbständig wissenschaftliche Methoden der IT-Sicherheit anwenden
• können die Ergebnisse wissenschaftlich in einem Referat präsentieren und sich einer Diskussion stellen
• können Herausforderungen in der Teamarbeit erkennen und reflektieren sowie angehen

Content:
Inhalte zu aktuellen Herausforderungen und Lösungen der IT Sicherheit mit ausgewählten Fragen der Orchestrierung als technisch-organisatorische Gestaltungsaufgabe wie zum Beispiel aus:

• Sicherheitsmanagement Standardisierung,
• Zertifizierung und Evaluation Technische Gestaltung vor Rechtlichen Erwartungshorizonten
• ethische und sozial Aspekte der IT-Sicherheit einschließlich Nachhaltigkeit, digitale Inklusion und Barrierefreiheit
• Sicherheit im E-Business
• Fallstudien zur IT-Sicherheit

Workload:
28 h Präsenzzeit 152 h selbstständige Arbeit

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
keine	Hausarbeit	Forschungsprojekt (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:
https://omen.cs.uni-magdeburg.de/itiamsl/deutsch/lehre/index.html

Sensor Networks Seminar

Winter

(in German: Sensor Networks Seminar )

Module-ID: FIN-INF-120514

Abbreviation SN

Credit Points 6

Semester Winter

Term ab 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Prof. Dr.-Ing. Benjamin Noack
Lecturer:	Prof. Dr.-Ing. Benjamin Noack
Classes:	Seminar Sensor Networks
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Schlüssel- und Methodenkompetenzen - M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027)

Intended learning outcomes:
Students who complete the course ...

• ​can independently research complex topics
• write clear scientific articles
• present informative and understandable scientific talks
• understand challenges and methods in sensor networks
• understand how to these challenges are addressed in practical applications of sensor networks

Content:
In this seminar, the participants will learn about

• challenges and methods used in sensor networks
• the variety of applications and application-specific sensor network design

Workload:
28 h in presence (consultation meetings, presentations) + 156 h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Presentation Discussion Scientific Article (Referat und Hausarbeit)	Seminar (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Lecture Introduction to Distributed Sensor Data Fusion Learning algorithms Underpinnings of engineering sciences and/or business informatics (some topics only) Principles of scientific writing

Media:	Literature:
	Literature depends on topic assignment and is given to each student together with the assignment

Comments:

Soft Skills für Masterstudierende

Sommer

(engl. Soft Skills for Masters students )

Modulnummer: FIN-INF-120250

Kürzel SK-M

CP 6

Semester Sommer

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Graham Horton
Dozent:in:	Graham Horton
Lehrveranstaltungen:	Vorlesung Soft Skills für Masterstudierende Tutorien Soft Skills für Masterstudierende
Verwendbarkeit:	- M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DE: Human Factors - M.Sc. DE: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. VC: Schlüssel- und Methodenkompetenzen - M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027)

Angestrebte Lernergebnisse:
Die Studierenden ...

• kennen die Grundlagen für eine wertbasierte Lebensführung
• können kreative Methoden der Ideengewinnung und Problemlösung durchführen
• können eine inhaltliche Planung für ihre Masterarbeit erstellen
• können Techniken des Selbstmanagements anwenden
• kennen Perspektiven für die Wahl ihres Berufs

Inhalt:

• Wissenschaftliches Arbeiten III + IV
• Persönliche Produktivität
• Life Leadership
• Problemlösungstechniken
• Wertschöpfung und Kundennutzen
• Innovation
• Querdenken
• Berufswahl
• Meetings leiten

Arbeitsaufwand:
56 h Präsenzzeit + 124 h selbstständige Arbeit

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
Gruppenarbeit Thesis proposal erarbeiten	Klausur 120 min	Vorlesung (2 SWS) Tutorien (2 SWS)

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

Hinweise:
Der frühere Name dieses Moduls war Schlüsselkompetenzen 3

Wissenschaftliches Teamprojekt

jedes

(engl. Scientific Team Project )

Modulnummer: FIN-INF-120482

Kürzel WTP

CP 6

Semester jedes

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Studiendekan:in der FIN
Dozent:in:	Dozenten der FIN
Lehrveranstaltungen:	keine
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INF: Projekte (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. WIF: Projekte (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DKE: Projects (SPO 2027) - M.Sc. DE: Interdisziplinäres Team-Projekt - M.Sc. DE: Digital Engineering-Projekt - M.Sc. DE: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DE: Projekte (SPO 2027) - M.Sc. VC: Schlüssel- und Methodenkompetenzen - M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. VC: Projekte (SPO 2027)

Angestrebte Lernergebnisse:
Nach Abschluss des Moduls können die Studierenden …

• im Team eine komplexe Aufgabe aus dem Fachgebiet mit wissenschaftlichen Methoden bearbeiten
• Lösungsmöglichkeiten diskutieren und sich auf einen gemeinsamen Lösungsweg einigen, zu dem jeder seine Ideen einbringt,
• das benötigte Wissen erarbeiten und für das Team verfügbar machen
• mit anderen zusammen ein Projekt planen und lenken und für ihre eigene Rolle im Team die Verantwortung übernehmen
• das Team-Ergebnis präsentieren und beurteilen und ihre Entscheidungen verteidigen
• sich rückblickend mit der Ausübung ihrer Rolle im Team und ihrem Beitrag zum gemeinsamen Ergebnis auseinandersetzen

Inhalt:
Dieses Modul wird von unterschiedlichen Hochschullehrern implementiert. Die fachlichen Inhalte sind daher angebotsspezifisch.

Arbeitsaufwand:
180 h selbstständige Arbeit

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
	Referat	Wissenschaftliches Projekt

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

Hinweise:

Scientific Teamproject KMD

every

(in German: Wissenschaftliches Teamprojekt KMD )

Module-ID: FIN-INF-120528

Abbreviation Teamproj_KMD

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Myra Spiliopoulou
Lecturer:	Myra Spiliopoulou
Classes:	Teamproj_KMD
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Interdisziplinäres Team-Projekt - M.Sc. DE: Fachliche Spezialisierung - M.Sc. DE: Digital Engineering-Projekt - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen

Intended learning outcomes:
When completing this module successfully, the students can: * solve a realistic data mining and data engineering task in teamwork * assess the business implications (costs and benefits) of a solution they propose * built-up a team and organize themselves in it, distributing subtasks among themselves according to their competences, setting milestones and pursuing a joint schedule * search for scientific papers of relevance * search appropriate libraries for data preparation, learning and visualization * design a learning workflow, apply AI methods and evaluate the induced models * develop a software solution jointly, using public domain tools * justify their design decisions and software suite selections * present their work as a team * write a joint report where they summarize and justify their approachBitte nachtragen nach neuen Constructive Alignment Vorgaben.

Content:
CONTENT: Assignment in the form of a realistic task that involves the analysis of static or dynamic, structured or unstructured data with mining methods, including supervised, unsupervised and semi-supervised methods, stream miners, forecasters and elaborate AI tools. The assignment involves design, development and evaluation of a software solution in teamwork. STRUCTURE: The assignment is for a team of students, typically three; for larger teams, the assignment is extended to ensure that the effort of each student is 6 ECTS. The students are called to solve the task as a team. The task involves understanding a realistic problem, designing, developing and evaluating a solution for it, presenting this solution to the class, defending it and documenting it on paper.

Workload:
For each team member: 28 h in class (including meetings) + 124 h self-study The teamproject is typically for three students. For larger teams, the assignments is extended to ensure that the effort of each team member is as above.

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Wissenschaftliches Projekt, implementiert als eine Hausarbeit, die ein Referat beinhaltet COURTESY TRANSLATION: This module implements the exam form 'Wissenschaftliches Projekt' as a 'Hausarbeit' which incorporates a 'Referat'.	Wissenschaftliches Teamprojekt (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Familiarity with advanced mining methods for static data and (for some assignments) for streams Familiarity with software development and software libraries Underpinnings of project management

Media:	Literature:
	Literature depends on topic assignment and is given to each team together with the assignment

Comments:
Als Implementierung des generischen PFLICHTMODULS "Wissenschaftliches Teamprojekt" entsprechend anrechenbar. COURTESY TRANSLATION: This module implements the COMPULSORY generic module 'Wissenschaftliches Teamprojekt'.

Master Thesis (Masterarbeit)

Masters Thesis

jedes

(in German: Masterarbeit )

Module-ID: FIN-INF-999997

Abbreviation

Credit Points 30

Semester jedes

Term ab 3.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Studiendekan:in der FIN
Lecturer:	Alle Dozenten der FIN
Classes:	keine
Applicability in curriculum:	- M.Sc. INF: Masterarbeit - M.Sc. INGINF: Masterarbeit - M.Sc. WIF: Masterarbeit - M.Sc. DKE: Masterarbeit - M.Sc. DE: Masterarbeit - M.Sc. VC: Masterarbeit

Intended learning outcomes:
Nach Abschluss des Moduls können die Studierenden …

• eine komplexe Aufgabe aus Anwendung oder Forschung mit wissenschaftlichen Methoden selbstständig bewältigen,
• sich in die Thematik selbstständig einarbeiten, die Aufgabenstellung analysieren und Lösungsmöglichkeiten dafür vergleichen
• einen eigenen Lösungsweg entwickeln und anschließend bewerten
• ihre Arbeitsergebnisse sowie deren potenzielle Konsequenzen einschätzen und bewerten.
• ihre Ergebnisse und das neue erworbene Wissen erklären und ihre Entscheidungen und Interpretationen verteidigen.

Content:
Durch die Bearbeitung der Masterarbeit entwickeln die Studierenden eine eigenständige Lösung und erwerben dadurch detailliertes und kritisches Verständnis in einem Spezialbereich der Informatik. Das Thema der Masterarbeit kann aus aktuellen Forschungsvorhaben der Institute oder aus betrieblichen Problemstellungen mit wissenschaftlichem Charakter abgeleitet werden. Dieses Modul wird von unterschiedlichen Hochschullehrern implementiert. Die fachlichen Inhalte sind daher angebotsspezifisch.

Workload:

• 22 Wochen eigenständige Erstellung einer wissenschaftlichen Arbeit
• 1h Verteidigung

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Masterarbeit und Verteidigung	selbstständige Arbeit, Verteidigung

Prerequisites according to examination regulations:	Recommended prerequisites:
Nachweis von 60 CP bzw. 90 CP aus den Schwerpunktbereichen (siehe SPO des Studienganges)

Media:	Literature:

Comments:

• Wie finde ich ein Thema und wie schreibe ich meine Masterarbeit?
• Übersicht für Masterarbeitsthemen im Thesis Portal

💬 Feedback

The module handbooks of the Faculty of Computer Science have been switched to a new system. We welcome feedback on this.

Yes, I can see ways to improve the new module handbook.

👑 Credits

Die maßgeblichen Beteiligten waren:

Daniel Lespin (🦸‍♂️ Style-Held)

Lukas Eichel (🦸‍♂️ Code-Held)

Christos Lachanas (🦸‍♂️ Code-Held)

Michael Preuß (🦸‍♂️ Technik-Held 1, um aus einer Testinstanz eine Lösung zu machen)

Heiko Dorwarth (🦸‍♂️ Technik-Held 2 für ein flüssiges Gitlab und Bookstack)

Graham Horton (🦸‍♂️ Aktualisierungs-Held)

Ich danke euch für dieses Engagement! 🙌

Jana (26. Juni 2026)