July 23, 2020: These pages have been updated for the 2020 edition of the course, except for the class-schedule. Due to the COVID-19 situation classes will be online, taking a different form from previous years. Be sure to attend the first class in MS Teams to get up-to-date information about the course form and what is expected of you.


Learning goals (2020-2021): upon completing this course, the student

  1. recognises and understands the strengths and weaknesses of probabilistic graphical models 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;
  4. has knowledge and understanding of methods for constructing the Bayesian network graph for actual applications;
  5. has knowledge and understanding of various methods for quantifying Bayesian networks, including parameterized models (noisy-or), together with their benefits and limitations;
  6. understands and is able to apply techniques for evaluating the robustness and quality of a Bayesian network.