Explainable Machine Learning

Explainable Machine Learning

 

Overview

In this seminar we will discuss different methodologies in Explainable Machine Learning, which is concerned with understanding what information a Machine Learning system learned 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 consistent of an introductory meeting with a lecture at the beginning of the semester introducing the field and distributing papers, and a three-day block course in the semester break covering paper and project presentations and discussions.

Students are expected to read into their assigned paper, the related literature, prepare a talk, a paper summary with critical discussion, and conduct a practical project around their topic.

 

Course Information

Semester:  WS

Year:  2026/27

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

Project showcase event:TBA

Essay and project hand-in deadline: TBA

 

Registration:     Registration is through the SIC seminar assignment system. Please do not directly register via the CMS.
                           

 

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

Teaching Assistants:         TBA

 

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.