SM 459: Seminar on Explainable AI Methods (HWS 2026)

This seminar will cover classical attribution methods in Explainable AI, including techniques such as saliency maps, feature importance, and gradient-based explanations.
It will focus on how these methods help interpret and analyze the decision-making process of machine learning models.

• This seminar is organized by Prof. Dr.-Ing. Margret Keuper
• Available for Bachelor Students
• Prerequisites:  Basic understanding in Machine Learning
• Maximum number of participants is 6 students

In this seminar, you will

• Read, understand, and discuss a basic topic relevant within computer vision
• Summarize this topic in a concise report (10 single-column pages + references)
• Give two presentations about your topic (3 minutes flash presentation, 15 minutes presentation)
• Moderate a scientific discussion about the topic of one of your fellow students
• Review a (draft of a) report of a fellow student

The the kick-off meeting will take place on 24. February 2026 at 17:15. 

• Seminar Schedule is provided here : Seminar Schedule (PDF, 189 kB)