Lecture: 2.01.597 Trustworthy Machine Learning - Details

Lecture: 2.01.597 Trustworthy Machine Learning - Details

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General information

Course name Lecture: 2.01.597 Trustworthy Machine Learning
Subtitle inf597
Course number 2.01.597
Semester SoSe2022
Current number of participants 14
expected number of participants 10
Home institute Department of Computing Science
Courses type Lecture in category Teaching
First date Thursday, 21.04.2022 08:15 - 09:45, Room: A14 1-114
Type/Form V+Ü
Lehrsprache deutsch

Rooms and times

A14 1-114
Monday: 12:15 - 13:45, weekly (12x)
Thursday: 08:15 - 09:45, weekly (13x)
(V04, R123)
Wednesday, 10.08.2022 08:15 - 12:15
Monday, 22.08.2022 10:00 - 11:00


Machine learning algorithms find its way into an increasing number of
(safety-critical) application domains but their quality is rarely assessed in a systematic way. The focus of this module are quality criteria for machine learning algorithms, in particular of deep neural networks, ranging from performance evaluation over explainability/interpretability (XAI), robustness (adversarial robustness, robustness against input perturbations), uncertainty quantification, distribution shift, domain adaptation, fairness/bias to privacy. The methods are introduced theoretically in the lecture and implemented/applied practically in the exercises. Prerequisites are basic theoretical knowledge in machine learning, programming skills in Python and ideally practical knowledge in training neural networks.

Admission settings

The course is part of admission "Anmeldung gesperrt (global)".
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The following rules apply for the admission:
  • Admission locked.