Module im Bereich: Methoden der Informatik
- Advanced Database Models
- Advanced Topics in Databases
- Advanced Topics in Deep Learning (Master)
- Advanced Topics in Networking
- Assistenzrobotik
- Clean Code Development
- Computational Geometry
- Data Mining I - Introduction to Data Mining
- Data Mining II - Advanced Topics in Data Mining
- Data Science with Python
- Datenbanken 2
- Estimation for Autonomous Mobile Robots
- Evolutionary Multi-Objective Optimization
- In-Memory and Cloud-Technologies
- Introduction to Deep Learning
- Introduction to Distributed Sensor Data Fusion
- Introduction to Machine Learning Safety
- Learning Generative Models
- MLOps for Small Language Model Applications
- Machine Learning
- Management of Global Large IT-Systems in International Companies
- 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
- Sensor Networks Seminar
- Software Defined Networking
- Software-Development for Industrial Robotics
- Three-Dimensional & Advanced Interaction
- VLBA – Cloud DevOps Technologies
- Visual Analytics
- Visual Analytics in Health Care
- Visualization
- Wissenschaftliches Individualprojekt
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: | false |
| Responsibility: | Gunter Saake |
| Lecturer: | Eike Schallehn |
| Classes: |
|
| 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) |
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
none |
Database introduction course |
| Media: | Literature: |
|
|
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: |
|
| 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 |
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
none |
|
| 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): |
|
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: | false |
| Responsibility: | David Hausheer |
| Lecturer: | David Hausheer |
| Classes: |
|
| 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 |
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
none |
Modul Computernetze |
| Media: | Literature: |
|
Textbooks as indicated.
Slides and paper copies as necessary.
|
Comments:
Assistenzrobotik
Sommer
(engl. Assistance robotics )
Modulnummer: FIN-INF-120443 |
|
Kürzel AROB |
CP 6 |
Semester Sommer |
Fachsem. ab 1. |
Dauer 1 Semester |
Sprache deutsch |
Niveau Master |
| Link zum LSF: | LSF |
| Zugang beschränkt: | |
| Verantwortung: | Norbert Elkmann (Fraunhofer IFF) |
| Dozent:in: | Norbert Elkmann (Fraunhofer IFF) |
| Lehrveranstaltungen: |
|
| Verwendbarkeit: | - M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: 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. DE: Informatik Wahlpflicht (SPO 2027) - M.Sc. DE: Digital Engineering (SPO 2027) - M.Sc. DE: Engineering Specialization (SPO 2027) |
Angestrebte Lernergebnisse:
Absolvent*innen dieses Moduls ...
- kennen mathematische Transformationen zur geometrischen und differentiellen Beschreibung von Roboterkinematiken
- kennen die Bewegungsgleichung von Roboter-Manipulatoren und können sie für Fragestellungen zur kollaborativen Sicherheit anwenden
- kennen die unterschiedlichen Interpolationsarten zum Beschreiben einer Roboterbahn und können eigenständig C²-Trajektorien generieren
- können die Kinematik von mobilen Robotern modellieren und ihre Position unter Berücksichtigung von Unsicherheiten im Raum bestimmen
- kennen verschiedene Methoden des maschinellen Lernens (unüberwachtes und überwachtes Lernen sowie Deep Learning) und können deren Einsatzmöglichkeiten für die Bildverarbeitung im Kontext der Robotik einordnen
- verstehen die Grundprinzipien des Reinforcement Learnings und wissen, wie damit optimale Handhabungsstrategien für Roboter generiert werden können
- kennen die Grundlagen der Maschinensicherheit (Risikobeurteilung und CE-Kennzeichnung)
- kennen die Sicherheitsbetriebsarten für kollaborative Robotersysteme und können sie anwenden
Inhalt:
- Einführung in die Assistenzrobotik
- Grundlagen der Assistenzrobotik (Modellierung von Roboterkinematiken, Bahnplanung, Bewegungs- und Kraftregelung, Sensoren, mobile Systeme)
- Mensch-Roboter-Kollaboration und Sicherheit: Technologien, Maschinensicherheit, Normen, Rechtslage
- KI-Verfahren in der Robotik
Arbeitsaufwand:
42h Präsenzzeit + 138h selbstständige Arbeit
| Prüfungsvorleistungen: | Studien-/Prüfungsleistungen: | Lehrform / SWS: |
|
Mündliche Prüfung
|
|
| Voraussetzungen nach Prüfungsordnung: | Empfohlene Voraussetzungen: |
|
keine
|
|
| Medienformen: | Literatur: |
|
|
|
Hinweise:
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: | true |
| Responsibility: | Prof. Dr. Frank Ortmeier |
| Lecturer: | Prof. Dr. Frank Ortmeier |
| Classes: |
|
| 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 |
|
| 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.
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: | false |
| 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 |
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
keine |
Basics in algorithmic geometry (for example, bachelor modul Grundzüge der Algorithmischen Geometrie) |
| Media: | Literature: |
|
Comments:
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: | false |
| Responsibility: | Myra Spiliopoulou |
| Lecturer: | Myra Spiliopoulou |
| Classes: |
|
| 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
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. |
|
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
keine |
|
| 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)
|
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: | false |
| Responsibility: | Myra Spiliopoulou |
| Lecturer: | Myra Spiliopoulou |
| Classes: | Vorlesung DM 2
Übung DM 2
COURTESY TRANSLATION:
|
| 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
Content:
Block 1A: Data Streams
- Basics
- Stream classification: learning methods and concept drift detectors; evaluation approaches
- Semi-supervised stream learning: methods and evaluation approaches
- Basics
- Time series classification
- Time series prediction
- Evaluation of models
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:
|
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
keine |
|
| 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: | false |
| 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. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Interdisziplinäres Team-Projekt |
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: |
|
| 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.)
|
|
| 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:
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: | false |
| Responsibility: | Prof. Dr.-Ing. Benjamin Noack |
| Lecturer: | Prof. Dr.-Ing. Benjamin Noack |
| Classes: |
|
| 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) |
|
| 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: |
|
| 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 |
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
none |
| Media: | Literature: |
|
Comments:
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: |
|
| 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 |
|
| 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: | true |
| Responsibility: | Sebastian Stober |
| Lecturer: | Sebastian Stober |
| Classes: |
|
| 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) |
|
| 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: |
|
| 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) |
|
| 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: |
|
| 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): |
|
|
| 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:
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: | true |
| 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
- Preparation and follow-up of lecture & exercise
- Development of an LLM-based sotution following LLMLOps practicies to address a business problems
| 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: |
|
|
| 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: |
|
| 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. |
|
| 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: |
|
| 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) |
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
keine |
Knowledge about IT-Systems and Business administration |
| Media: | Literature: |
|
|
Comments:
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: |
|
| 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
|
|
| 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: | true |
| Responsibility: | Martin Glauer |
| Lecturer: | Martin Glauer |
| 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): |
|
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: |
|
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: | false |
| 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: | false |
| 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
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: | false |
| Responsibility: | Myra Spiliopoulou |
| Lecturer: | Myra Spiliopoulou |
| Classes: |
|
| 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
- 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
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) |
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
keine |
|
| Media: | Literature: |
|
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: | true |
| Verantwortung: | Prof. Dr. Frank Ortmeier |
| Dozent:in: | Prof. Dr. Frank Ortmeier |
| Lehrveranstaltungen: |
|
| 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: | false |
| 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: | false |
| 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
- collect additional literature from the topic area in a transparent manner
- compare the scientific advances in the collected literature in order to answer the scientific questions
- critically discuss and rank the methods compared in the previous step
- present the collected literature, the comparisons and the results in class
- 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 |
|
| 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: | true |
| Responsibility: | Prof. Dr.-Ing. Benjamin Noack, Prof. Dr. Myra Spiliopoulou |
| Lecturer: | Prof. Dr.-Ing. Benjamin Noack, Prof. Dr. Myra Spiliopoulou |
| Classes: |
|
| 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 |
|
| Media: | Literature: |
|
Literature depends on topic assignment and is given to each student together with the assignment
|
Comments:
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: | true |
| 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): |
|
Seminar (2 SWS) |
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
keine |
|
| 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: | false |
| Responsibility: | David Hausheer |
| Lecturer: | David Hausheer |
| Classes: |
|
| 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 |
|
| 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: | true |
| Verantwortung: | Prof. Dr. Frank Ortmeier |
| Dozent:in: | Prof. Dr. Frank Ortmeier |
| Lehrveranstaltungen: |
|
| 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
|
|
| Voraussetzungen nach Prüfungsordnung: | Empfohlene Voraussetzungen: |
|
none
|
|
| Medienformen: | Literatur: |
|
|
|
Hinweise:
Zuangsbeschränkung: Limitierte Teilnehmerzahl. Auswahlverfahren wird in erster Vorlesung bekannt gegeben
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: | true |
| Responsibility: | Christian Hansen |
| Lecturer: | Florian Heinrich |
| Classes: |
|
| 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 |
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
keine |
Module Interactive Systems |
| Media: | Literature: |
|
Comments:
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: | true |
| Responsibility: | Prof. Dr. Klaus Turowski |
| Lecturer: | Prof. Dr. Klaus Turowski |
| Classes: |
|
| 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:
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: | true |
| Responsibility: | Bernhard Preim |
| Lecturer: | Bernhard Preim |
| Classes: |
|
| 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 |
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
none |
|
| Media: | Literature: |
|
|
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: | true |
| 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: | true |
| Responsibility: | Bernhard Preim |
| Lecturer: | Bernhard Preim |
| Classes: |
|
| 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 |
|
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
none |
|
| 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.