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Trustworthy Machine Learning

Machine learning has made great advances over the past year and many techniques have found their ways into applications. This leads to an increasing demand of techniques that not only perform well - but are also "trustworthy".

Trustworthiness includes:
- Interpretability of the prediction
- Robustness against changes to the input, which occur naturally or with malicious intend
- Privacy preserving machine learning (e.g. when dealing with sensitive data such as in health applications)
- Fairness
- ...



As a proseminar’s primary purpose is to learn presentation skills, the seminar will feature two presentations from each student. As presentation and writing skills are highly interlink for each presentation also a very short - at most 2 pages - report has to be handed in.

In the first half of the semester, we will have presentations of two topics each week. After each presentation, fellow students and lecturers will provide feedback on how to improve the presentation. This general feedback must then be taken into account for the second half of the semester, where again each student will present.



The *first presentations and report* will count towards 30% of the overall grade, the *second presentation and report* will count towards 70% of the overall grade. Attendance in the proseminar meetings is mandatory. At most one session can be skipped, after that you need to bring a doctor’s note to excuse your absence.



The date for the meeting is fixed to Thursday, 14-16. All meetings will be virtualized via Zoom. Here are the details for joining the virtual meetings.
Meeting ID: 942 9134 5987
Password: 7K?7MS



May 7th Kick off Meeting and topic overview (slides, video)
May 14th How to present and write (slides, video)
May 21th Holiday
May 28th Analysing and dissection writing and presentation (paper1, video1, paper2, video2, seminar video)
June 4th no seminar
June 11th Holiday
June 18th (first round) Interpretability, Adversarial Examples, DeepFakes, Model Stealing
June 25th (first round) Uncertainty, Privacy, Fairness, Causality
July 2nd no seminar
July 9th (second round) Interpretability, Adversarial Examples, DeepFakes, Model Stealing
July 16th (second round) Uncertainty, Privacy, Fairness, Causality


First Round Papers (in random order)

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