Explainable Machine Learning

Overview

SemesterWinter 2022
Course typeBlock Seminar
LecturerJun.-Prof. Dr. Wressnegger
AudienceInformatik Master & Bachelor
Credits4 ECTS
Room148, Building 50.34
LanguageEnglish or German
Linkhttps://campus.kit.edu/campus/all/event.asp?gguid=0xB5C7C25A3A7C4464A36349B86FFDDA7B
Registrationhttps://ilias.studium.kit.edu/goto.php?target=crs_1922847&client_id=produktiv

Description

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.

Schedule

DateStep
Tue, 25. Oct, 11:30–13:00Primer on academic writing, assignment of topics
Thu, 3. NovArrange appointment with assistant
Mon, 7. Nov - Fri, 11. Nov1st individual meeting (First overview, ToC)
Mo, 5. Dec - Fri, 9. Dec2nd individual meeting (Feedback on first draft of the report)
Thu, 22. DecSubmit final paper
Mon, 9. JanSubmit review for fellow students
Thu, 12. JanEnd of discussion phase
Fri, 13. JanNotification about paper acceptance/rejection
Fri, 27. JanSubmit camera-ready version of your paper
Fri, 17. FebPresentation at final colloquium

Mailing List

News about the seminar, potential updates to the schedule, and additional material are distributed using a separate mailing list. Moreover, the list enables students to discuss topics of the seminar.

You can subscribe here.

Topics

Every student may choose one of the following topics. For each of these, we additionally provide a recent top-tier publication 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.

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