Interested in doing a (thesis) project related to this course? See the projects page.

Academic year 2026/2027:

Interested in following this course in the academic year 2026/2027?

These pages are not yet up-to-date, but an impression of the course can be found on last year's pages.

Learning goals (2026-2027): upon completing this course, the student

  1. recognises and understands the strengths and weaknesses of probabilistic graphical models (PGMs) in general and Bayesian networks in particular;
  2. understands the relation between probabilistic independence and the graphical representations thereof, and is able to draw conclusions from this relation;
  3. understands and is able to apply probabilistic inference in Bayesian networks and through probabilistic programs;
  4. has knowledge and understanding of methods for constructing probabilistic models for actual applications;
  5. understands and is able to apply techniques for evaluating the robustness and quality of probabilistic models.