Thesis Projects (Bachelor and Masters)
See the thesis project page.
(Summer 2025) Bridging Theory and Practice: Reliable ML in Data Corruption Scenarios
This is a Master-level seminar focused on reliable machine learning under data corruption, combining theory and practice in areas such as medicine, social systems, and fairness. It begins with a kick-off lecture covering key tools from statistics and learning theory, followed by team-based projects exploring types of corruption—such as sample selection bias or concept drift—using a “Data Corruption Bingo” format. The course concludes with group presentations and a short paper. Alma 2025 site. Taught by Nan Lu and Laura Iacovissi.
(Summer 2024) A Zoo of Non-Standard Learning Problems
This is a Master-level seminar exploring a range of overlooked but intriguing supervised learning problems—such as superset learning, multi-instance learning, label ranking, ordinal regression, anomaly detection, learning with soft labels, and learning under heavy-tailed class distributions. Other topics include learning with coherent risk measures and the option to reject or defer decisions to humans. Students choose one topic to explore in depth through a presentation and a hands-on coding project. Taught by Rabanus Derr and Christian Fröhlich.
(Winter 2021 , Winter 2023) Beyond Fairness: A Socio-Technical View of Machine Learning
This is a Masters course that starts off by looking at various mathematisations of fairness in ML, and proceeds to examine many other more general issues, in particular the role of data and categorisation. A wide literature is drawn from and the assessment is a team term paper. Moodle site 2023. Taught by Bob Williamson. Tutors: Nan Lu , Benedikt Höltgen and Sebastian Zezulka
(Winter 2022 , Winter 2023) Mathematics for Machine Learning
This is a course providing the necessary mathematical background for the Master of Machine Learning students. Ilias site. Taught by Armando Cabrera Pacheco.
(Winter 2022) Information Theory
This is a Bachelor course that covers the source and channel coding theorems and the role of information theory in machine learning. Taught by Bob Williamson.