Lecture: Machine Learning
Overview
This course introduces students to the fundamental concepts, techniques, and algorithms in machine learning. It covers the mathematical and theoretical foundations, supervised and un-supervised learning techniques, evaluation methods, and advanced aspects. Students will gain hands-on experience in implementing, training, and optimizing machine learning models using real-world datasets.
Organization
Lecturer: Prof. Dr. A. Bojchevski
Time: Tuesdays, 16:00 - 17:30 and Wednesdays, 16:00 - 17:30
Place: Hörsaal II, Physics Institute
Seminar: Trustworthy Machine Learning
Overview
Machine learning models are increasingly used in safety-critical applications and to make automated decisions about humans. Beyond accuracy and efficiency, we expect such models to also be robust to noise and adversaries, to faithfully represent their (aleatoric and epistemic) uncertainty, to preserve privacy, to be fair w.r.t. different demographic groups, and to be interpretable. In this seminar, we will cover the latest research on these trustworthiness aspects, as well as the (fundamental) trade-offs between them. We will study the shortcomings and failures of traditional machine learning models and how to improve them.
Organization
Lecturer: Prof. Dr. A. Bojchevski
Time: Mondays, 08:00 - 09:30
Place: Room 1.421, Building 415 (Sibille-Hartmann-Str. 2-8)
Introductory Presentation Meeting (Vorbesprechung): tbd