Semester | Summer 2023 |
Course type | Block Seminar |
Lecturer | Jun.-Prof. Dr. Wressnegger |
Audience | Informatik Master & Bachelor |
Credits | 4 ECTS |
Room | 148, Building 50.34 |
Language | English |
Link | TBA |
Registration | TBA |
This seminar is concerned with explainable machine learning in computer security. Learning-based systems often are difficult to interpret, and their decisions are opaque to practitioners. This lack of transparency is a considerable problem in computer security, as black-box learning systems are hard to audit and protect from attacks.
The module introduces students to the emerging field of explainable machine learning and teaches them to work up results from recent research. To this end, the students will read up on a sub-field, prepare a seminar report, and present their work at the end of the term to their colleagues.
Topics cover different aspects of the explainability of machine learning methods for the application in computer security in particular.
Date | Step |
Tue, 18. April, 9:45–11:15 | Primer on academic writing, assignment of topics |
Thu, 27. April | Arrange appointments with assistant |
Tue, 02. May - Fri, 05. May | 1st individual meeting (First overview, ToC) |
Mon, 05. June - Fri, 09. June | 2nd individual meeting (Feedback on first draft of the report) |
Wed, 28. June | Submit final paper |
Mon, 10. July | Submit review for fellow students |
Fri, 14. July | End of discussion phase |
Fri, 21. July | Submit camera-ready version of your paper |
Fri, 28. July | Presentation at final colloquium |
News about the seminar, potential updates to the schedule, and additional material are distributed using the course's matrix room. Moreover, matrix enables students to discuss topics and solution approaches.
You find the link to the matrix room on ILIAS.
Every student may choose one of the following topics. For each of these, we additionally provide two recent top-tier publications that you should use as a starting point for your own research. For the seminar and your final report, you should not merely summarize that paper, but try to go beyond and arrive at your own conclusions.
Moreover, all of these papers come with open-source implementations. Play around with these and include the lessons learned in your report.