inf5402 - Trustworthy Machine Learning (Complete module description)

Module label	Trustworthy Machine Learning
Module code	inf5402
Credit points	6.0 KP
Workload	180 h
Institute directory	Department of Computing Science
Applicability of the module	Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik Master's Programme Computing Science (Master) > Angewandte Informatik
Responsible persons	Strodthoff, Nils (module responsibility) Lehrenden, Die im Modul (authorised to take exams)
Prerequisites	Content requirements are basic theoretical knowledge in machine learning, practical programming knowledge in Python basic knowledge in deep neural network training.
Skills to be acquired in this module	Professional competence The students have an overview of the various aspects that determine the quality of machine learning algorithms. are familiar with methods to measure different quality aspects and, if necessary, methods to enhance them, and they can implement and apply these methods. Methodological competence The students independently develop theoretical and practical concepts with the help of in-person events, provided materials, and specialized literature. Social competence The students can present solution approaches for problems in this area to the plenary and defend them in discussions. Self-competence The students are able to assess their own subject-specific and methodological competence. They take responsibility for their competence development and learning progress and reflect on these independently. In addition, they independently work on learning content and can critically reflect on the content.
Module contents	Machine learning algorithms are increasingly being applied in a wide range of areas, particularly in safety-critical domains. However, the quality of these algorithms is rarely systematically examined. The focus of this event is on various quality dimensions for machine learning algorithms, especially deep neural networks. This ranges from performance measurement to interpretability/explainability (XAI), robustness (adversarial robustness, non-adversarial robustness, distribution shifts, OOD-detection), uncertainty quantification, fairness/bias, and privacy. The methods will be introduced theoretically in the lecture and practically implemented and applied in the exercises.
Recommended reading	As there is no single textbook that covers all topics in this lecture series, relevant specialized readings will be recommended throughout the course.
Links
Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	Wintersemester
Module capacity	unlimited
Teaching/Learning method	V+Ü

Type of course	SWS	Frequency	Workload of compulsory attendance
Lecture	2	WiSe	28
Exercises	2	WiSe	28
Total module attendance time			56 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	At the end of the lecture term	Written or oral Exam