Explainable Machine Learning

Explainable Machine Learning

 

Overview

In this seminar we will discuss different methodologies in Explainable Machine Learning, concerned with understanding what information a Machine Learning system learns and how it uses this information for decision making. We cover 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, and conduct a practical project around the assigned topic.

 

Course Information

Semester:  WS

Year:  2025/26

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:  TBA

Block seminar: TBA

 

Registration:     Registration through the SIC seminar assignment system.
                           

 

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

Teaching Assistants:          Sukrut Rao, Amin Parchami-Araghi, Nhi Pham

 

Material

We recommend
Interpretable Machine Learning - A Guide for Making Black Box Models Explainable by Christoph Molnar
The book can be accessed for free online (external link), and as part of the Semesterapparat of the UdS Math and CS library.