News
Reminder for project proposalsWritten on 21.11.25 by Sukrut Sridhar Rao Dear students of ExML,
We hope you had a great start into your semester! The first deadline for our seminar is approaching slowly, the projects will be fixed on 5th of December. In that light, please make sure to reach out to your supervisor to schedule a meeting within the next two weeks for… Read more Dear students of ExML,
We hope you had a great start into your semester! The first deadline for our seminar is approaching slowly, the projects will be fixed on 5th of December. In that light, please make sure to reach out to your supervisor to schedule a meeting within the next two weeks for the project proposal. We highly encourage you to come up with your own project which we can discuss and steer in a meaningful direction.
All the best for any upcoming midterms!
The ExML team |
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: Wednesday 29th October, 2025; 10AM - 12PM; E1. 5, Room 029
Block seminar: TBA
Registration: Registration through the SIC seminar assignment system.
Lecturer: Dr. Jonas Fischer, Prof. Dr. Bernt Schiele
Teaching Assistants: Amin Parchami-Araghi, Nhi Pham, Sukrut Rao, Dr. Wolfgang Stammer
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.