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Announcement: Essay Review Comments Sent – Please Revise and SubmitWritten on 02.03.25 by Hanwei Zhang Dear all, The review comments for each of your essays have been sent via email. Please check your inbox, review the feedback carefully, and revise your essay accordingly. Submit your revised essay through the dCMS system by the deadline. If you use any general AI tools to assist with your… Read more Dear all, The review comments for each of your essays have been sent via email. Please check your inbox, review the feedback carefully, and revise your essay accordingly. Submit your revised essay through the dCMS system by the deadline. If you use any general AI tools to assist with your revisions, please attach the usage log along with your submission. If you have any questions, feel free to reach out. Best, Hanwei |
Exploring Explainability in Machine Learning
Registration:
If you are interested in taking this seminar, please register after 11.09.2024 on the faculty's page for seminar assignments: https://seminars.cs.uni-saarland.de/seminars2425.
Registration directly in the dCMS is not possible. You will receive a token from us to register in dCMS once you have been successfully assigned a slot in this seminar by the system.
Registration in LSF/HISPOS
You must register in LSF/HISPOS no later than 3 weeks after the kick-off event.
Otherwise, we will not be able to record your grades at the end of the semester.
If your degree program does not use LSF/HISPOS, you will receive an old-fashioned paper certificate to present to your examination office.
Please contact us early regarding this.
Overview
This seminar course delves into the crucial and evolving field of explainability in machine learning (ML). As ML models become increasingly complex and integral to various domains, understanding how these models make decisions is essential. This course will explore different methodologies for interpreting ML models, including rule-based, attribution-based, example-based, prototype-based, hidden semantics-based, and counterfactual-based approaches. Through a combination of paper readings, discussions, and presentations, students will gain a comprehensive understanding of the challenges and advancements in making ML models transparent and interpretable.
Requirements:
- The student should take a course in machine learning or have sufficient knowledge from other courses;
- The student should speak English and understand that the seminar will be conducted entirely in English.
Places: 8
Schedule
(Could be changed)
| Week | Start | Stop | Date | Event |
| 1 | 21.10.24 | 25.10.24 |
23.10.24 16:00 - 18:00 E1 3, Room 528 |
Kickoff + Tutorial |
| 2 | 28.10.24 | 01.11.24 | Bid + Assign | |
| 3 | 04.11.24 | 08.11.24 | Read | |
| 4 | 11.11.24 | 15.11.24 | Read | |
| 5 | 18.11.24 | 22.11.24 | Prepare Story | |
| 6 | 25.11.24 | 29.11.24 | Meet | |
| 7 | 02.12.24 | 06.12.24 | Prepare Slides | |
| 8 | 09.12.24 | 13.12.24 | Prepare Slides | |
| 9 | 16.12.24 | 20.12.24 | Presentation 1&2 | |
| - | Christmas Break | |||
| - | Christmas Break | |||
| 10 | 06.01.25 | 10.01.25 | Presentation 3&4 | |
| 11 | 13.01.25 | 17.01.25 | Presentation 5&6 | |
| 12 | 20.01.25 | 24.01.25 | Presentation 7&8 | |
| 13 | 27.01.25 | 31.01.25 | Retrospective |
Submission Deadlines:
Report: Feb. 15
Review Opinion: Feb. 28
Revised Report: Mar. 15