M.Sc. Data and Knowledge Engineering (ab Winter 2026/27)

• Modulhandbuch
• 👋 Herzlich willkommen
• 🧭 Orientierung
• Über den Studiengang
• Orientierung am Regelstudienplan
• 📰 Neuigkeiten
• Modulnachrichten
• Nutzung von generativer KI für Abschlussarbeiten und Prüfungs(vor)leistungen
• Fachschaftsrat der Fakultät für Informatik (Studierendenvertretung)
• Academic Club
• ⚠️ Regelungen des Prüfungsausschusses
• Module im Bereich: Fundamentals of Data Science
• ⚠️ Hinweise zu Modulen im Bereich: Fundamentals of Data Science
• Data Mining I - Introduction to Data Mining
• Data and Knowledge Engineering
• Introduction to Simulation
• Logik 2
• Machine Learning
• Principles and Practices of Scientific Work and Soft Skills
• Module im Bereich: Learning Methods and Models for Data Science
• Advanced Topics in Deep Learning (Master)
• Applied Discrete Modelling
• Data Mining II - Advanced Topics in Data Mining
• Evolutionary Multi-Objective Optimization
• Introduction to Deep Learning
• Introduction to Machine Learning Safety
• Learning Generative Models
• Neuro-Symbolic Integration
• Recommenders
• Seminar 'Advanced Topics of KMD'
• Seminar on Advanced Topics of Predictive Maintenance
• Swarm Intelligence
• Wissenschaftliches Individualprojekt
• Module im Bereich: Data Processing for Data Science
• Advanced Database Models
• Advanced Topics in Databases
• Advanced Topics in Deep Learning (Master)
• Computational Geometry
• Introduction to Data Warehousing
• Datenbanken 2
• Distributed Data Management
• In-Memory and Cloud-Technologies
• Seminar 'Advanced Topics of KMD'
• VLBA – Cloud DevOps Technologies
• Wissenschaftliches Individualprojekt
• Module im Bereich: Applied Data Science
• AMS Lab Projects
• Advanced Programming in Computational Intelligence in Games
• Advanced Topics in Deep Learning (Master)
• Assistenzrobotik
• Clean Code Development
• Data Science with Python
• Design-Projekt
• Estimation for Autonomous Mobile Robots
• Flow Visualization
• Human-Centred Manufacturing and Usability
• IT-Security of Cyber-Physical Systems
• Introduction to Distributed Sensor Data Fusion
• MLOps for Small Language Model Applications
• Management of Global Large IT-Systems in International Companies
• Musik Information Retrieval
• Narrative Visualization
• Praktikum IT Sicherheit
• Recent Topics in Business Informatics
• Scientific Project on Databases for Multi-Dimensional Data, Genomics, and modern Hardware
• Scientific Writing
• 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
• Service Engineering
• Software-Development for Industrial Robotics
• Three-Dimensional & Advanced Interaction
• VLBA - Systemarchitekturen
• VLBA – Cloud DevOps Technologies
• Visual Analytics
• Visual Analytics in Health Care
• Visualization
• Wissenschaftliches Individualprojekt
• Wissenschaftliches Individualprojekt (10 CP)
• Wissenschaftliches Teamprojekt
• Scientific Teamproject KMD
• Masterarbeit
• Masters Thesis
• 💬 Feedback
• 👑 Credits

Modulhandbuch

für den Master of Science (M.Sc.) Data and Knowledge Engineering

der Fakultät für Informatik

gültig für die SPO 2024 und ältere SPOs*

verbindlich für das Wintersemester 2026/27 und vorläufig für das Sommersemester 2027

Stand 01.10.2026

* Für ältere SPOs als die SPO 2024 gelten abweichende Regelstudienpläne, die entsprechend berücksichtigt werden müssen. Beachten Sie insbesondere abweichende Pflichtveranstaltungen und CP. Nicht zu Ihrer SPO passende Lehrveranstaltungen können ggf. nicht vollständig oder gar nicht angerechnet werden. Prüfen Sie auch die Zuordnungen aus dem Modulhandbuch des Wintersemesters 2024/2025. Wenden Sie sich bei Inkonsistenzen oder Unklarheiten an das Prüfungsamt.

👋 Herzlich willkommen

Lieber Studierende,

herzlich willkommen zum Modulhandbuch für deinen Studiengang.

Das Modulhandbuch umfasst das Angebot aller Module, die du brauchst, um dein Studium erfolgreich abzuschließen. Ein Modul ist dabei immer eine Lehreinheit, die aus mehreren Lehrveranstaltungen (z.B. Vorlesung und Übung) besteht, um sich Kompetenzen und Fähigkeiten im Studium anzueignen. Alle Module sind im Modulhandbuch genauer beschrieben, so dass du dein Studium genauer planen kannst.

🗺️ Was finde ich hier?

In deinem Modulhandbuch findest du:

1. Ein 🧭 Orientierungskapitel, das den Studiengang zusammenfasst, dir Orientierung an einem beispielhaften Regelstudienplan bietet, mögliche Studienprofile für deinen Studiengang aufzeigt sowie Module von fachfremden Fakultäten auflistet. 
2. Das Kapitel 📰 Neuigkeiten teilt dir Änderungen an Modulen im Vergleich zum vorigen Semester mit, Hinweise zur Nutzung von KI, Informationen der Fachschaft und weiteres.
   
3. Außerdem sind die ⚠️ Regelungen des Prüfungsausschuss verlinkt.
4. In den Kapiteln Module im Bereich [...] bis zum Kapitel Abschlussmodule werden dann die Module aufgelistet, die laut Regelstudienplan Pflicht oder Wahlpflicht für deinen Studiengang sind.
5. Eine Seite mit einem Link zu einem Feedback-Formular, um Anregungen zu sammeln, die unsere Modulhandbücher in Zukunft weiter verbessern.
   

⭐ Wie kann ich das nutzen?

1. Diese Übersicht hilft beim Planen eines und auch zukünftiger Semester. So werden die meisten Module nicht jedes Semester angeboten. Manche bauen auch aufeinander auf. Das benötigt ein vorausschauendes Planen, wenn du eine fachliche Vertiefung studieren möchtest.
   
2. Im Wahlpflichtbereich kann auch die Form der Prüfung darüber Aufschluss geben die eigenen Kapazitäten für die Prüfungsbelastung zu berücksichtigen.
3. Zum Prüfen, welche Module bereits erfolgreich absolviert wurden und welche noch für einen erfolgreichen Abschluss des Studiums fehlen.

❓Und was ist mit ...?

Wenn es darüber hinaus Fragen gibt, die du selbst mit dem Modulhandbuch oder der Studien- und Prüfungsordnung nicht lösen kannst, wende dich gern an ...

• ... deinen Studiengangsleiter für Fragen zum Studium oder Verwendbarkeit von Modulen oder
  
• ... unser Prüfungsamt für individuelle studiumsbezogene Fragen.
  

🏆 Die Fakultät wünscht dir maximalen Erfolg für dein Studium!

🧭 Orientierung

Über den Studiengang

Studienziel

Im Masterstudiengang Data and Knowledge Engineering bieten wir begabten Studierenden solide Fachkenntnisse zu den theoretischen Aspekten und den Anwendungsgebieten der Wissensentdeckung, des maschinellen Lernens, des Data Mining und Warehousing, der unterstützenden Datenbanktechnologie und der Sprachen und Modellen für die Repräsentation von Daten, Information und Wissen.Durch Praxisbezug, interdisziplinäre Veranstaltungen und Projektarbeiten lernen unsere Studierende die Herausforderungen von Anwendungen aus der Wirtschaft, Naturwissenschaften, Biotechnologie, Sicherheit, industrieller Fertigung und Kontrolle und erwerben die Fertigkeiten zur Gestaltung von innovativen Lösungsansätzen in selbständiger Arbeit und in Gruppenarbeit.

Spätere Berufsfelder

Unseren Absolventen und Absolventinnen steht eine Vielfalt von Berufen zur Auswahl - als Wissensingenieure in großen Einrichtungen wie Banken, Automobilhersteller und Medizinzentren, als Projektleiter und als Berater in interdisziplinären Projekten von großen oder kleineren Unternehmen, sowie die Perspektive einer akademischen Karriere.

Notwendige Kenntnisse / Erfahrungen / Interessen

siehe Zulassungsvoraussetzungen

Weitere Informationen

Hier geht es zu weiteren Studiendokumenten.

Orientierung am Regelstudienplan

Die Grafik zeigt den Regelstudienplan mit Studienbeginn im Wintersemester. Verlinkt sind alle Module und Modulbereiche. Links im Winter-/Sommersemester führen unter Umständen zum Wechsel des Modulhandbuchs. Das dient einer besseren Orientierung und Studienplanung. Der Regelstudienplan nach SPO ist unter Studiendokumente zu finden.

Beispiel für Beginn im Wintersemester

1. Semester	2. Semester	3. Semester	4. Semester
Fundamentals of Data Science (6 CP)	Learning Methods & Models for Data Science (6 CP)	Learning Methods & Models for Data Science (6 CP)	Masterarbeit (30 CP)
Fundamentals of Data Science (6 CP)	Learning Methods & Models for Data Science (6 CP)	Learning Methods & Models for Data Science (6 CP)
Learning Methods & Models for Data Science (6 CP)	Data Processing for Data Science (6 CP)	Data Processing for Data Science (6 CP)
Learning Methods & Models for Data Science (6 CP)	Applied Data Science (6 CP)	Data Processing for Data Science (6 CP)
Data Processing for Data Science (6 CP)	Applied Data Science (6 CP)	Applied Data Science (6 CP)

Die Module sind zu thematischen Gruppen zusammengefasst und müssen folgende Mindest- und Maximalanzahlen erreicht werden:

• Fundamentals of Data Science (12-18 CP)
• Learning Methods & Models for Data Science (18-36 CP)
• Applied Data Science (18-30 CP)
• Data Processing for Data Science (18-24 CP)

Das Modul Wissenschaftliches Teamprojekt ist obligatorisch und ist ausschliesslich dem Bereich Applied Data Science zuzuordnen.

📰 Neuigkeiten

Modulnachrichten

Neue Module in diesem Semester

Module, die in diesem Semester nicht angeboten werden

Moduländerungen

Nutzung von generativer KI für Abschlussarbeiten und Prüfungs(vor)leistungen

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

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

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

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

Fachschaftsrat der Fakultät für Informatik (Studierendenvertretung)

Academic Club

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

More information on the Academic Club.

⚠️ Regelungen des Prüfungsausschusses

Diese Sammlung von Regelungen fasst relevante Beschlüsse des Prüfungsausschusses der Fakultät für Informatik zusammen. Im Zweifelsfall gilt der Wortlaut der Beschlüsse des Prüfungsausschusses.

Sie sind hier zu finden.

Module im Bereich: Fundamentals of Data Science

⚠️ Hinweise zu Modulen im Bereich: Fundamentals of Data Science

Im Folgenden werden Module für den Bereich Fundamentals of Data Science aus anderen Fakultäten aufgelistet. Wir verweisen auf die Modulbeschreibungen der jeweiligen anbietenden Fakultät; sie können in den Modulhandbüchern der entsprechenden Fakultäten gefunden werden.

Abweichende Module müssen vom Studiengangsleitenden genehmigt werden.

Mathematik

• Statistical Methods (Statistical Methods II) (Winter)

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:	Vorlesung DM1_ENG Übung DM1_ENG COURTESY TRANSLATION: Lecture class DM1_ENG Exercise class DM1_ENG
Applicability in curriculum:	- M.Sc. DKE: Fundamentals of Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

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

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

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

Content:

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

Workload:

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

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

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

Media:

Literature:

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

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

Data and Knowledge Engineering

Sommer

(in German: Data and Knowledge Engineering )

Module-ID: FIN-INF-120500

Abbreviation

Credit Points 6

Semester Sommer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

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

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

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

• Document organization
• Information management
• Ranking & Explainability

Natural Language Processing

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

Human Computer Interaction

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

Workload:
180 h self study

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

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:

Introduction to Simulation

Winter

(in German: Introduction to Simulation )

Module-ID: FIN-INF-120345

Abbreviation ItS

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Graham Horton
Lecturer:	Graham Horton
Classes:	Lecture Introduction to Simulation Exercise class Introduction to Simulation
Applicability in curriculum:	- M.Sc. DKE: Fundamentals of Data Science - M.Sc. DE: Grundlagen Informatik - M.Sc. DE: Informatik Grundlagen (SPO 2027)

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

• can analyse discrete, continuous and hybrid systems using a professional simulation tool
• know how to obtain an accurate result efficiently when integrating initial value problems numerically
• can carry out input and output analysis to obtain statistically significant results for stochastic, discrete-event models
• can select an appropriate modelling paradigm for a given situation
• can perform a simulation study and interpret its results

Content:
Introduction to the fundamentals and steps in a simulation study:

• discrete-event simulation
• random variables and random number generation
• statistical data analysis
• ordinary differential equations and numerical integration
• stochastic Petri nets
• the AnyLogic simulation system
• discrete-time Markov chains
• agent-based simulation

Workload:
56 contact hours + 124 h self study

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

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Basic engineering mathematics

Media:	Literature:
	Banks, Carson, Nelson, Nicol: Discrete-Event System Simulation

Comments:

Logik 2

Winter

(engl. Logic 2 )

Modulnummer: FIN-INF-0

Kürzel Logik2

CP 10

Semester Winter

Fachsem. ab 4.

Dauer 1 Semester

Sprache deutsch

Niveau Bachelor,Master

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

Angestrebte Lernergebnisse:

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

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

Arbeitsaufwand:

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

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

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

Medienformen:	Literatur:

Hinweise:
Anmeldung via E-Learning-Plattform

Machine Learning

Winter

(in German: Machine Learning )

Module-ID: FIN-INF-120517

Abbreviation ML

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
Students ...

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

Content:

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

Workload:
56 contact hours + 124h self study

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

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

Media:	Literature:

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

Principles and Practices of Scientific Work and Soft Skills

Summer

(in German: Principles and Practices of Scientific Work and Soft Skills )

Module-ID: FIN-INF-120502

Abbreviation PPSW

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:	true
Responsibility:	PD Dr. Claudia Krull
Lecturer:	PD Dr. Claudia Krull
Classes:	Lecture PPSW Exercise PPSW
Applicability in curriculum:	- M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DKE: Fundamentals of Data Science - M.Sc. DE: Human Factors - M.Sc. DE: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027)

Intended learning outcomes:

• Students know how a Goal is defined and are able to formulate it properly. based on that students know methods for time management.
• Students understand important aspects of project management and teamwork, including phases and pitfalls.
• Students are able to conduct a systematic literature review in order to gain an overview of an area of interest.
• Students are able to identify and formulate research hypothesis
• Students understand how a scientific publication is structured, can read it and extract the important information.
• Students are able to write a research proposal on a selected topic in the form of a scientific publication.
• Students understand how to present a research topic proposal on a selected topic.

Content:
The course covers the following topics, among others:

• Introduction to Scientific Work
• Literature Research and Management Research Projects and Thesis Topics
• Scientific Writing - Thesis Structure and Writing Techniques
• Study Skills & Self Management
• Project Management & Team Work
• Presentation Skills

The project and term paper topic can be related to an ongoing research project or be used for Master’s thesis preparation.

Workload:
40 h in person teaching 140 h independent work

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
participation in all classes completion of assignments keeping all deadlines	Report	Lecture (2 SWS) Exercise (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:
participation is limited to 40

Module im Bereich: Learning Methods and Models for Data Science

Advanced Topics in Deep Learning (Master)

every

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

Module-ID: FIN-INF-120496

Abbreviation ATDL

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
Students ...

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

Content:

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

Workload:

• 30h in person
• 150h self-study

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

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

Media:	Literature:

Comments:

Applied Discrete Modelling

Winter

(in German: Applied Discrete Modelling )

Module-ID: FIN-INF-120279

Abbreviation ADM

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:

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

Content:

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

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

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

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

Media:	Literature:
recorded lectures and exercises are available

Comments:

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: Lecture class DM2 Exercise class DM2
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

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

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

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

Content:
Block 1A: Data Streams

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

Block 1B: Time series

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

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

Workload:

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

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

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

Media:	Literature:

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

Evolutionary Multi-Objective Optimization

Summer

(in German: Evolutionary Multi-Objective Optimization )

Module-ID: FIN-INF-120469

Abbreviation EMO

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
The students...

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

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

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

Workload:
56 h contact time + 124 h self study

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

Prerequisites according to examination regulations:	Recommended prerequisites:
none

Media:

Literature:

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

Comments:

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:	Lecture Introduction to Deep Learning Exercise class Introduction to Deep Learning
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
Students ...

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

Content:

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

Workload:

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

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

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

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

Comments:

Introduction to Machine Learning Safety

Sommer

(in German: Introduction to Machine Learning Safety )

Module-ID: FIN-INF-120532

Abbreviation MLS

Credit Points 6

Semester Sommer

Term ab 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:

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

Content:

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

Workload:

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

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

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

Media:	Literature:

Comments:

Learning Generative Models

Winter

(in German: Learning Generative Models )

Module-ID: FIN-INF-120522

Abbreviation LGM

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
Students can ..

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

Content:

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

Workload:
56 contact hours + 124h self study

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

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

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

Comments:

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

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

Content:

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

Workload:
Total: 180 h

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

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

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

Media:

Literature:

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

Comments:

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:	Lecture Recommenders Exercise class Recommenders
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

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

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

TECHNICAL EXPERTISE: They

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

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

Content:
The course consists of two blocks:

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

Workload:

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

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

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

Media:

Literature:

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

Comments:

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

The student must:

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

Workload:

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

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

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

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

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

Seminar on Advanced Topics of Predictive Maintenance

every

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

Module-ID: FIN-INF-120489

Abbreviation PredMa_Masterseminar

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

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

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

Content:
In this seminar, the participants will learn about

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

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

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

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

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

Comments:

Swarm Intelligence

Winter

(in German: Swarm Intelligence )

Module-ID: FIN-INF-102419

Abbreviation SI

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

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

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

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

Part 2: Swarm and multiagent systems

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

Part 3: Applications

• Swarm localization and display
• Swarm robotics

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

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

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

Media:

Literature:

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

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

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.

Module im Bereich: Data Processing for Data Science

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:	Lecture Advanced Database Models Exercise Advanced Database Models
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Data Processing for Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
The students ...

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

Content:

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

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

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

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

Media:	Literature:

Comments:

Advanced Topics in Databases

Summer

(in German: Advanced Topics in Databases )

Module-ID: FIN-INF-120277

Abbreviation ATDB

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
The students ...

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

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

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

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

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

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

Media:	Literature:

Comments:

Advanced Topics in Deep Learning (Master)

every

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

Module-ID: FIN-INF-120496

Abbreviation ATDL

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
Students ...

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

Content:

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

Workload:

• 30h in person
• 150h self-study

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

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

Media:	Literature:

Comments:

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	Lecture and Exercise (4 SWS)

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

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

Comments:

Introduction to Data Warehousing

Winter

(in German: Data Warehouse Technologies )

Module-ID: FIN-INF-102808

Abbreviation DWT

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:
Responsibility:	Gunter Saake
Lecturer:	David Broneske
Classes:	Lecture Introduction to Data Warehousing Exercise Introduction to Data Warehousing
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. DE: Fachliche Spezialisierung

Intended learning outcomes:
The students ...

• understand the data warehousing approach
• understand technologies in the field of data warehouses
• can use data warehouse specific DBMS functionalities
• can design and develop data warehouse applications

Content:

• The data warehouse approach
• Architecture and differences to traditional Database Management
• The Extract-Transform-Load process
• OLAP and the Multidimensional data model
• Application of the model in database systems
• Query execution and optimization
• Index and Storage structures for data warehouses
• Buisiness Intelligence on data warehouses

Workload:
56 contact hours + 124 h self study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Written exam (120 min)	2 SWS Lecture 2 SWS Exercise class

Prerequisites according to examination regulations:	Recommended prerequisites:
keine	Modules Datenbanken 1/Database Concepts and Datenbanken 2

Media:	Literature:
	Data Warehouse Technologien. Veit Köppen, Gunter Saake, Kai-Uwe Sattler. 2. Auflage, mitp-Verlag, 2014

Comments:

Datenbanken 2

Sommer

Datenbankimplementierungstechniken

(engl. Database Implementation Techniques )

Modulnummer: FIN-INF-120531

Kürzel DB II

CP 6

Semester Sommer

Fachsem.

Dauer 1 Semester

Sprache deutsch

Niveau Master

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

Angestrebte Lernergebnisse:

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

Inhalt:

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

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

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

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine	Datenbanken 1

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

Hinweise:

Distributed Data Management

Winter

(in German: Distributed Data Management )

Module-ID: FIN-INF-102404

Abbreviation DDM

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
The students ...

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

Content:

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

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

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

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

Media:	Literature:

Comments:

In-Memory and Cloud-Technologies

Winter

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

Module-ID: FIN-INF-120506

Abbreviation IMCloud

Credit Points 6

Semester Winter

Term starting at 1.

Duration 2 Semester

Language english

Level Master

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

Intended learning outcomes:
Die Studierenden ...

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

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

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

Workload:
Präsenzzeiten = 56 h:

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

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

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

Media:

Literature:

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

Comments:
Start jeweils zum Wintersemester.

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

The student must:

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

Workload:

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

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

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

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

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

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:	'- Lecture VLBA Cloud DevOps Technologies Exercise VLBA Cloud DevOps Technologies Consultation VLBA - Cloud DevOps Technologien (Consultation) Tutorial VLBA - Cloud DevOps Technologien (Tutorial)
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. DKE: Data Processing for Data Science - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Intended learning outcomes:
Students ...

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

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

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

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

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:

Wissenschaftliches Individualprojekt

jedes

(engl. Individual Scientific Project )

Modulnummer: FIN-INF-120301

Kürzel WIP

CP 6

Semester jedes

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

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

Angestrebte Lernergebnisse:
Die Studierenden ...

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

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

Arbeitsaufwand:
180h selbstständige Arbeit

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

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

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

Module im Bereich: Applied Data Science

AMS Lab Projects

every

(in German: AMS Lab Projects )

Module-ID: FIN-INF-120503

Abbreviation AMS-Lab

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

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

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

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

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

Workload:
180 h of individual and team work

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

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

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

Comments:

Advanced Programming in Computational Intelligence in Games

Summer

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

Module-ID: FIN-INF-120495

Abbreviation AP-CIG

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
Students ...

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

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

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

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

Workload:
56 contact hours + 94h self study

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

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

Media:

Literature:

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

Comments:

Advanced Topics in Deep Learning (Master)

every

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

Module-ID: FIN-INF-120496

Abbreviation ATDL

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
Students ...

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

Content:

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

Workload:

• 30h in person
• 150h self-study

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

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

Media:	Literature:

Comments:

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:	Vorlesung Assistenzrobotik Übung Assistenzrobotik
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	2 SWS Vorlesung 2 SWS Übung

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine	Programmierkenntnisse Lineare Algebra

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:	Vorlesung Clean Code Development Übung Clean Code Development
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen

Intended learning outcomes:
Students:

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

Content:

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

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

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

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

Media:	Literature:

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

Data Science with Python

Winter

(in German: Data Science with Python )

Module-ID: FIN-INF-120513

Abbreviation DSWP

Credit Points 6

Semester Winter

Term

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:	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. DE: Methoden der Informatik - M.Sc. DE: Interdisziplinäres Team-Projekt - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen

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

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

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

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

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

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

Media:	Literature:
	Will provided during the seminar

Comments:

Design-Projekt

Sommer

(engl. Design Project )

Modulnummer: FIN-INF-120520

Kürzel DSP

CP 6

Semester Sommer

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

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

Angestrebte Lernergebnisse:
Absolvent*innen dieses Moduls...

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

Inhalt:

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

Arbeitsaufwand:
Präsenzzeiten:

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

Selbstständiges Arbeiten:

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

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

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

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

Hinweise:

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:	Lecture Estimation for Autonomous Mobile Robots Exercise Estimation for Autonomous Mobile Robots
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INF: Informatik Wahlpflicht (SPO 2027) - M.Sc. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. WIF: Informatik - M.Sc. WIF: Informatik Wahlpflicht (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. DE: Informatik Wahlpflicht (SPO 2027) - M.Sc. DE: Digital Engineering (SPO 2027) - M.Sc. DE: Engineering Specialization (SPO 2027) - M.Sc. VC: Computer Science - M.Sc. VC: Computer Science (SPO 2027)

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

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

Content:

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

Workload:
56 h contact time 124 h independent study

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

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

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

Comments:

Flow Visualization

Summer

(in German: Flow Visualization )

Module-ID: FIN-INF-120261

Abbreviation FlowVis

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
The students

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

Content:

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

Workload:
56 contact hours + 124h self study

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

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

Media:	Literature:

Comments:

Human-Centred Manufacturing and Usability

Sommer

HCAMU

(in German: Human-Centred Manufacturing and Usability )

Module-ID: FIN-INF-120512

Abbreviation HCMU

Credit Points 6

Semester Sommer

Term

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
The students ...

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

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

Workload:

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

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

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:

IT-Security of Cyber-Physical Systems

Winter

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

Modulnummer: FIN-INF-120457

Kürzel ITS-CPS

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

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

Angestrebte Lernergebnisse:
Die Studierenden

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

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

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

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

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

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

Medienformen:	Literatur:

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

Introduction to Distributed Sensor Data Fusion

Summer

(in German: Introduction to Distributed Sensor Data Fusion )

Module-ID: FIN-INF-120497

Abbreviation SDF

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

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

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

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

Workload:
56 h contact time 124 h independent study

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

Prerequisites according to examination regulations:	Recommended prerequisites:
none

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

Comments:

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

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

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

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

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

Media:	Literature:

Comments:

Management of Global Large IT-Systems in International Companies

Summer

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

Module-ID: FIN-INF-120315

Abbreviation MGLIIC

Credit Points 6

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
Students are able to …

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

Content:

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

Workload:
56 contact hours + 124 h self study

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

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

Media:	Literature:

Comments:

Musik Information Retrieval

Winter

(engl. Music Information Retrieval )

Modulnummer: FIN-INF-120516

Kürzel MIR

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

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

Angestrebte Lernergebnisse:
Die Studierenden ...

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

Inhalt:

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

Arbeitsaufwand:

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

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

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

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

Hinweise:

Narrative Visualization

Summer

(in German: Narrative Visualization )

Module-ID: FIN-INF-120523

Abbreviation NarVis

Credit Points 3

Semester Summer

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
Students ...

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

Content:

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

Workload:
28 h contact hours + 152 h self study

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

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

Media:	Literature:

Comments:

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

independent work = 140 hr:

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

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

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

Media:	Literature:

Comments:

Scientific Project on Databases for Multi-Dimensional Data, Genomics, and modern Hardware

every

(in German: Scientific Project on Databases for Multi-Dimensional Data, Genomics, and modern Hardware )

Module-ID: FIN-INF-120507

Abbreviation SPDB

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:	true
Responsibility:	Gunter Saake
Lecturer:	Gunter Saake
Classes:	Lecture Scientific Project on Databases for Multi-Dimensional Data, Genomics, and modern Hardware
Applicability in curriculum:	- M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Interdisziplinäres Team-Projekt - M.Sc. DE: Fachliche Spezialisierung

Intended learning outcomes:
The students ...

• work and coordinate in a team
• write academic papers
• present scientific results
• acquire knowledge of an advanced research topic on their own
• self-sufficiently develop solutions on selected topics of databases or their applications
• assess their own work critically

Content:

• Scientific writing
• Survey of research literature
• Advanced research topics in the area of databases, genomics and modern hardware

Workload:
contact hours: 14h lectures + 8h presentations + 14h consultations
self study: 60h implementation + 24h presentations + 30h papers

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

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Programming Knowledge in C++, Python, Java or Rust

Media:	Literature:

Comments:
Requirement: Passing an initial programming test

Scientific Writing

Winter

(in German: Scientific Writing )

Module-ID: FIN-INF-120511

Abbreviation SciWri

Credit Points 6

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:

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

Content:

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

Workload:

• 28h Seminar
• 152h independent work

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

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:
participation is limited to 40

Scrum-in-Practice

Winter

(engl. Scrum-in-Practice )

Modulnummer: FIN-INF-120438

Kürzel SIP

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

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

Angestrebte Lernergebnisse:
Die Studierenden ...

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

Inhalt:

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

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

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

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine	Software Engineering

Medienformen:	Literatur:

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

Selected Chapters of IT Security: Orchestration of Mechanisms and Tools

Winter

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

Module-ID: FIN-INF-120505

Abbreviation ITSEC-Orch

Credit Points 6

Semester Winter

Term ab 1.

Duration 1 Semester

Language english

Level Master

Link:	LSF
Limited accessability:	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

The student must:

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

Workload:

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

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

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

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

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

Seminar on Advanced Topics of Predictive Maintenance

every

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

Module-ID: FIN-INF-120489

Abbreviation PredMa_Masterseminar

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

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

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

Content:
In this seminar, the participants will learn about

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

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

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

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

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

Comments:

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):
	Presentation Discussion Scientific Article (Referat und Hausarbeit)	Seminar (2 SWS)

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

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

Comments:

Service Engineering

Sommer

(engl. Service Engineering )

Modulnummer: FIN-INF-120530

Kürzel

CP 6

Semester Sommer

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:
Verantwortung:	Prof. Dr. Frank Ortmeier
Dozent:in:	Prof. Dr. Andreas Schmietendorf (HWR Berlin)
Lehrveranstaltungen:	Vorlesung Service Engineering Übung Service Engineering
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science

Angestrebte Lernergebnisse:
Die Studierenden ...

• kennen die Auswirkungen serviceorientierter Architekturansätze auf den gesamten Lebenszyklus derart implementierter Lösungen,
• kennen Herausforderungen (Technologie, Prozesse, Organisation, …) serviceorientierter Integrationslösungen und einhergehende Lösungsalternativen,
• können benötigte Services modellgetrieben herleiten und erstellen bzw. Kriterien basiert bewerten (make or buy),
• kennen aktuelle Technologieansätze zur Implementierung benötigter Services und können ausgewählte Vertreter prototypisch anwenden,
• können Servicekompositionen aus Sicht der Modellierung und Implementierung (Low- und High –Code) erstellen bzw. das benötigte Management anwenden.

Inhalt:
Überblick

• Die Vorlesung vermittelt zunächst die geschäfts-, organisations- und prozessbezogenen Ursachen API-basierter Integrationslösungen. In diesem Zusammenhang wird auf domänenspezifische (Referenz-) Modellansätze aus dem industriellen Umfeld (Telekommunikation, Banken, Versicherungen, …) eingegangen.
• Darauf aufbauend lernen die Studierenden die Funktionen von Integrationsplattformen (u.a. API-Management) kennen, welche API-Kompositionen über deren gesamten Lebenszyklus unterstützen bzw. verwalten.
• Mit Hilfe der ersten Komplexübung erfolgt dann eine quantitative Bewertung im Internet angebotenen Public- bzw. Open APIs.
• Eine weitere alternativ gestaltbare Komplexübung beschäftigt sich mit der Spezifikation, Modellierung, Entwicklung und Einbindung webbasierter APIs in (ggf. mobile) Applikationsszenarien.
• Im Mittelpunkt der abschließenden Komplexübung steht die Komposition von APIs, wobei abermals ein eigener Schwerpunkt (Modellsicht, Implementierung, Management) gewählt werden kann.

Themen:

• Integrationslösungen als Treiber der Digitalisierung
• Integrieren statt Implementieren
• Basistechnologien für Web-APIs (Public- und Open APIs)
• Spezifikation, Entwicklung und Management von Web-APIs
• Prozess-Modellierung und -Implementierung (API-Komposition)

Arbeitsaufwand:
56 h Präsenz + 124 h selbstständige Arbeit

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
	Mündliche Prüfung	2 SWS Vorlesung 2 SWS Übung

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine	Software Engineering

Medienformen:

Literatur:

Hartenstein, S.; Nadobny, K.; Schmidt, S.; Schmietendorf, A.: Sicherheits- und Compliance-Management im Lebenszyklus von Web APIs, Logos-Verlag, Berlin, März 2020, Nitze, A.; Schmietendorf, A.: Bewertungsaspekte bei der agilen SW-Entwicklung …, Logos Verlag, Berlin 2014 Schmietendorf, A.; Knuth, M.: Aspekte des Software Engineerings im Diskurs einer Low-Code orientierten Softwareentwicklung, Logos Verlag, Berlin 2024 Sneed, H.; Sneed, S.: Web-basierte Systemintegration, Vieweg-Verlag, Braunschweig/Wiesbaden 2003 Spichale, K.: API-Design, Praxishandbuch für Java-und Webservice-Entwickler, dpunkt.verlag, 2017 Tilkov, S. et al.: REST und HTTP, dpunkt.verlag, Heidelberg 2015

Hinweise:

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:	Vorlesung Software-Development for Industrial Robotics Übung Software-Development for Industrial Robotics
Verwendbarkeit:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Angestrebte Lernergebnisse:
The students have ...

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

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

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

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

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
none

Medienformen:	Literatur:

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

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:	Lecture Three-Dimensional & Advanced Interaction Seminar Three-Dimensional & Advanced Interaction
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Visual Computing

Intended learning outcomes:
Students ...

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

Content:

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

Workload:
56h contact time + 124h self study

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

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

Media:

Literature:

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

Comments:

VLBA - Systemarchitekturen

Sommer

(engl. VLBA: Systemarchitectures )

Modulnummer: FIN-INF-120252

Kürzel VLBA-SysA

CP 9

Semester Sommer

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

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

Angestrebte Lernergebnisse:
Die Studierenden ...

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

Inhalt:

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

Arbeitsaufwand:
Präsenzzeiten:

28 h Vorlesung / 28 h Übung

Selbstständiges Arbeiten:

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

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

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

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

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

Hinweise:

VLBA – Cloud DevOps Technologies

Winter

(in German: VLBA – Cloud DevOps Technologies )

Module-ID: FIN-INF-120478

Abbreviation VLBA-CDOT

Credit Points 9

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
Students ...

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

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

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

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

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:

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:	Lecture Visual Analytics Exercise class Visual Analytics
Applicability in curriculum:	- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Visual Computing

Intended learning outcomes:
Students ...

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

Content:

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

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

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

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

Media:

Literature:

Powerpoint presentation Whiteboard Videos

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

Comments:

Visual Analytics in Health Care

Winter

(in German: Visual Analytics in Health Care )

Module-ID: FIN-INF-120476

Abbreviation VAHC

Credit Points 3

Semester Winter

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

Intended learning outcomes:
Students ...

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

Content:

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

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

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

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

Media:

Literature:

Powerpoint presentation Sketches Videos

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

Comments:

Wissenschaftliches Individualprojekt

jedes

(engl. Individual Scientific Project )

Modulnummer: FIN-INF-120301

Kürzel WIP

CP 6

Semester jedes

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

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

Angestrebte Lernergebnisse:
Die Studierenden ...

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

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

Arbeitsaufwand:
180h selbstständige Arbeit

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

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

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

Wissenschaftliches Individualprojekt (10 CP)

jedes

(engl. Individual Scientific Project )

Modulnummer: FIN-INF-999851

Kürzel WIP

CP 10

Semester jedes

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Link zum LSF:	LSF
Zugang beschränkt:	false
Verantwortung:	Studiendekan:in der FIN
Dozent:in:	Alle Dozenten der FIN
Lehrveranstaltungen:	keine
Verwendbarkeit:	- M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. INF: Projekte (SPO 2027) - M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. WIF: Projekte (SPO 2027) - M.Sc. DKE: Applied Data Science - M.Sc. DKE: Projects (SPO 2027) - M.Sc. DE: Schlüssel- und Methodenkompetenz (SPO 2027) - M.Sc. DE: Projekte (SPO 2027) - 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:
300 h selbständige Arbeit

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

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

Hinweise:

Wissenschaftliches Teamprojekt

jedes

(engl. Scientific Team Project )

Modulnummer: FIN-INF-120482

Kürzel WTP

CP 6

Semester jedes

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

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

Angestrebte Lernergebnisse:
Nach Abschluss des Moduls können die Studierenden …

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

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

Arbeitsaufwand:
180 h selbstständige Arbeit

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

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
keine

Medienformen:	Literatur:

Hinweise:

Scientific Teamproject KMD

every

(in German: Wissenschaftliches Teamprojekt KMD )

Module-ID: FIN-INF-120528

Abbreviation Teamproj_KMD

Credit Points 6

Semester every

Term starting at 1.

Duration 1 Semester

Language english

Level Master

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

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

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

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

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

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

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

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

Masterarbeit

Masters Thesis

jedes

(in German: Masterarbeit )

Module-ID: FIN-INF-999997

Abbreviation

Credit Points 30

Semester jedes

Term ab 3.

Duration 1 Semester

Language english

Level Master

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

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

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

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

Workload:

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

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

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

Media:	Literature:

Comments:

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

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Ich danke euch für dieses Engagement! 🙌

Jana (26. Juni 2026)