Module im Bereich: Informatik

• AMS Lab Projects
• Advanced Topics in Deep Learning (Master)
• Advanced Topics in Networking
• Applied Discrete Modelling
• Ausgewählte Algorithmen der Computergraphik
• Clean Code Development
• Computer-Assisted Surgery
• Data Mining II - Advanced Topics in Data Mining
• Data Science with Python

AMS Lab Projects

(in German: AMS Lab Projects )

Module-ID: FIN-INF-999912

Link:	LSF
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. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Interdisziplinäres Team-Projekt - M.Sc. DE: Digital Engineering-Projekt - M.Sc. VC: Computer Science

Abbreviation AMS-Lab

Credit Points 6

Semester every

Term

Duration 1 Semester

Language english

Level Master

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
• 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):
	Software and Documentation Project report Two Presentations	Project (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

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

Comments:

Advanced Topics in Deep Learning (Master)

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

Module-ID: FIN-INF-120496

Link:	LSF
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. DE: Methoden der Informatik - M.Sc. VC: Computer Science

Abbreviation ATDL

Credit Points 6

Semester jedes

Term ab 1.

Duration 1 Semester

Language english

Level Master

Intended learning outcomes:
Students ...

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

Content:

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

Workload:

• 30h in person
• 150h self-study

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

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

Media:	Literature:

Comments:

Advanced Topics in Networking

(in German: Advanced Topics in Networking )

Module-ID: FIN-INF-140014

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

Abbreviation ATN

Credit Points 6

Semester Winter

Term ab 1.

Duration 1 Semester

Language english

Level Master

Intended learning outcomes:
Students...

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

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

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

Workload:

• 28h lectures
• 6h practical exercises
• 146h homework (further studies, theoretical exercises, preparation for the final exam)

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

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

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

Comments:

Applied Discrete Modelling

(in German: Applied Discrete Modelling )

Module-ID: FIN-INF-120279

Link:	LSF
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. 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

Abbreviation ADM

Credit Points 6

Semester Winter

Term ab 1.

Duration 1 Semester

Language english

Level Master

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:

Ausgewählte Algorithmen der Computergraphik

(engl. Selected Algorithms in Computer Graphics )

Modulnummer: FIN-INF-120272

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

Kürzel AACG

CP 6

Semester Winter

Fachsem. ab 1.

Dauer 1 Semester

Sprache deutsch

Niveau Master

Angestrebte Lernergebnisse:
Die Studierenden

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

Inhalt:

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

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

Arbeitsaufwand:

• 56 h Präsenz
• 124 h selbstständige Arbeit

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

Voraussetzungen nach Prüfungsordnung:

Empfohlene Voraussetzungen:

keine

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

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

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

Clean Code Development

(in German: Clean Code Development )

Module-ID: FIN-INF-120432

Link:	LSF
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

Abbreviation CCD

Credit Points 6

Semester Winter

Term ab 1.

Duration 1 Semester

Language english

Level Master

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	Vorlesung (2 SWS) Übung (2 SWS)

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

Media:	Literature:

Comments:

Computer-Assisted Surgery

(in German: Computer-Assisted Surgery )

Module-ID: FIN-INF-140003

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

Abbreviation CAS

Credit Points 6

Semester Winter

Term ab 1.

Duration 1 Semester

Language english

Level Master

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

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

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

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

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

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

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

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

Workload:
56 h contact hours + 124 h self study

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

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

Comments:

Data Mining II - Advanced Topics in Data Mining

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

Module-ID: FIN-INF-120455

Link:	LSF
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: Fachliche Spezialisierung - M.Sc. VC: Computer Science

Abbreviation DM2

Credit Points 6

Semester Winter

Term ab 1.

Duration 1 Semester

Language english

Level Master

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 1: Data Streams

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

Block 2: Time series

• Basics
• Prediction methods
• Evaluation of predictors

Workload:
56 h Präsenz + 124 h selbstständige Arbeit COURTESY TRANSLATION: 56 h in class + 124 h self-study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Klausur 120 Minuten Prüfungszulassungsvoraussetzung: Erreichen einer minimalen Anzahl von Punkten durch Votierung auf Übungsaufgaben COURTESY TRANSLATION: Written exam of the form 'Klausur' with a duration of 120 min Prerequisite for the written exam: a minimum number of points must be achieved during the exercise classes; 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:

Block 1: Gama, J. (2010) Knowledge discovery from data streams. CRC Press. Silva, J. A., Faria, E. R., Barros, R. C., Hruschka, E. R., Carvalho, A. C. D., & Gama, J. (2013). Data stream clustering: A survey. ACM Computing Surveys (CSUR), 46(1), 1-31. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4), 1-37. Lemaire, V., Salperwyck, C., & Bondu, A. (2014, July). A survey on supervised classification on data streams. In European Business Intelligence Summer School (pp. 88-125). Springer, Cham. Žliobaitė, I., Bifet, A., Read, J., Pfahringer, B., & Holmes, G. (2015). Evaluation methods and decision theory for classification of streaming data with temporal dependence. Machine Learning, 98(3), 455-482. Murilo Gomes, H., Grzenda, M., Mello, R., Read, J., Nguyen, M. H. L., and Bifet, A. (2022). A survey on semi-supervised learning for delayed partially labelled data streams. ACM Computing Surveys, 55(4). - for Block 1D Block 2: Parmezan, A. R. S., Souza, V. M., & Batista, G. E. (2019). Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information sciences, 484, 302-337. Cismondi, F. C., Fialho, A., Vieira, S., Reti, S., Sousa, J., Finkelstein, S. (2013). Missing data in medical databases: Impute, delete or classify? Artificial Intelligence in Medicine. http://dx.doi.org/10.1016/j.artmed.2013.01.003. Further citations at the individual units: see the slidesets of the units.

Comments:

Data Science with Python

(in German: Data Science with Python )

Module-ID: FIN-INF-

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

Abbreviation DSWP

Credit Points 6

Semester Winter

Term

Duration 1 Semester

Language english

Level Master

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):
	Project with presentation and project report	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: