(Summer 2024) A Zoo of Non-Standard Learning Problems

In the block seminar "A Zoo of Nonstandard Learning Problems" we will explore less-known learning problems. In the vast landscape of machine learning, many problems often remain overshadowed by mainstream research, despite their practical significance and theoretical richness. This seminar aims to shed light on these neglected territories, focusing primarily on supervised learning settings.


  1. Superset Learning
  2. Multi-Instance Learning
  3. Label Ranking
  4. Ordinal Regression
  5. Learning with Soft Labels
  6. Multi-Label Learning
  7. Anomaly Detection
  8. Learning with Heavy-Tailed Class Distribution
  9. Learning with Interval (Imprecise) Input Data
  10. Learning with Coherent Risk Measures as Aggregation Functionals
  11. Learning with Option to Reject
  12. Learning with Option to Defer to Human

If interested to join the block seminar, send us a mail to rabanus.derr[at]uni-tuebingen.de ! 

Each student will choose a topic of interest from the list and give a presentation about it to the fellow students. After the presentations, each student will then dive deeper into their chosen paradigm through a hands-on coding project. The grading therefore consists of two parts: presentation and coding project. Join us on this journey as we broaden our horizons and explore a variety of learning problems!

Dates: preparatory meeting on the 30th April 17:00 - 18:00 (Meeting Room 1st floor Maria-von-Linden-Strasse 6) -> still open topics (send a mail to rabanus.derr[at]uni-tuebingen.de),
main dates 21st June 13:00 - 18:00 + 22nd June 10:00 - 15:00 (Meeting Room 3rd floor Maria-von-Linden-Strasse 6).
Credits: 3 ECTS
Workload: giving a presentation and coding project.

1. Send us a mail to: rabanus.derr[at]uni-tuebingen.de
2. Select topic on first-come-first-serve basis on a doodle-link announced via mail on a fixed date (more information via mail).

This course is a course at the master level and has a maximum capacity of 12 slots. Students in the M.Sc. Machine Learning will be preferentially admitted, but master students in Computer Science, Media Informatics, Medical Informatics, and Bioinformatics can also join the course if they have sufficient background knowledge in Statistical Machine Learning, Probabilistic Machine Learning and Deep Learning.

(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.