inf5452 - Current Topics in Trustworthy Machine Learning (Complete module description)

Module label	Current Topics in Trustworthy Machine Learning
Module code	inf5452
Credit points	3.0 KP
Workload	90 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 Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction Master's Programme Engineering of Socio-Technical Systems (Master) > Human-Computer Interaction Master's Programme Engineering of Socio-Technical Systems (Master) > Systems Engineering
Responsible persons	Strodthoff, Nils (module responsibility) Lehrenden, Die im Modul (authorised to take exams)
Prerequisites	The seminar requires attending a foundational lecture in the field of Machine Learning and/or Deep Learning.
Skills to be acquired in this module	Professional competence The students gain an exemplary overview of challenges and existing solution approaches in their respective problem domains and can contextualize these within the broader methodological context. Methodological competence The students can independently explore topics using current research literature and critically reflect upon them. 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	This seminar provides insights into various aspects of trustworthy Machine Learning. Depending on the instantiation of the module, different focuses should be set, such as interpretability/explainability, uncertainty quantification, or robustness.
Recommended reading
Links
Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	every winter term
Module capacity	unlimited
Teaching/Learning method	S

Examination	Prüfungszeiten	Type of examination
Final exam of module	at the end of the lecture period/ intermediate exams	oral exam / portfolio / presentation

Type of course	Seminar
SWS	2
Frequency	see frequency of module offering