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Written on 10.12.25 by Sukrut Sridhar Rao

Dear students of ExML,

We hope you have an enjoyable time with holidays coming up! Over the last weeks, we have been in touch with all of you to finalize the practical projects for the seminar, so you now have a clear project plan for the rest of the semester. Make sure to check with your… Read more

Dear students of ExML,

For GPU access, please carefully read the documentation provided in the course material section in CMS. Your GPU credentials can be found in your student page in the CMS.

Both the essays as well as projects will be due first of March 2026.

We currently plan the block course date to be on 10th and 11th of March 2026, please inform us about any potential exam clashes asap. Participation is mandatory.

On 12th of March 2026 we plan the project discussion as a showcase event (participation is mandatory), where each of you presents your respective project to your peers – this is supposed to be a fun event and less of an exam situation, you all work on exciting topics that are at the forefront of the field, and are interesting to us.

If you have specific questions about anything related to projects, or about your assigned topic, do not hesitate to reach out to us.

Enjoy the holiday season 🙂

Best,

the ExML team

Reminder for project proposals

Written 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 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.