Overview

In this seminar we will discuss different methodologies in Explainable Machine Learning, which is concerned with understanding what information a Machine Learning system learns and how it uses this information for decision making. We cover both seminal works as well as recent advancements in the field, including post-hoc explainability approaches and inherently interpretable model designs.

The seminar will consist of an introductory meeting with a lecture at the beginning of the semester introducing the field and distributing papers, and a two-day block course in the semester break covering paper presentations and discussions. Students are expected to read into their assigned paper, the related literature, prepare a talk as well as a paper summary with critical discussion.

Course Information

Semester:  SS

Year:  2024

Requirements: The student has a solid understanding of Machine Learning and feels comfortable with Neural Networks (for example through lectures High Level Computer Vision, Neural Networks: Theory and Implementation, or Machine Learning).

Time and location:

Introductory lecture:  23.04.24  14:00 - 16:00    E1.4 room 024 (MPII lecture hall)

Block seminar:  05.08.24  &  06.08.24  10:00 - 16:00   E1.4 room 021 (MPII seminar room)

Registration:     Registration through the SIC seminar assignment system.

Lecturer:           Dr. Jonas Fischer, Prof. Dr. Bernt Schiele