I got the opportunity to teach the Bayesian Networks module in the master course Knowledge Representation at VU.

One of the three modules in the compulsory master course *Knowledge Representation* is an introduction to Bayesian networks. For the 2022 iteration of this course, I got the opportunity to take over this module from Erman Acar. It was great fun to, for the first time, be responsible for the content of a whole module. Especially, building my own story around the content was a challenging but insightful task.

In the first lecture, I introduce the students to beliefs, their dynamics, and foundational properties of probability. With classical examples, I motivate the notion of independence and also formalize it. Lastly, I presented some basic tools for inference on Bayesian networks.

The second lecture is dedicated to a basic introduction to *directed acyclic graphs*, independence in graphs, a formal definition of Bayesian networks, and d-separation.

Before tackling more interesting inferences in Bayesian networks, I introduced more tools that are useful for that end. I introduce *factors *as a convenient object for various inference algorithms, talk about *variable elimination*, the importance of good elimination orderings, and graph pruning.

In the concluding lecture, I treated more interesting queries for Bayesian networks: *Posterior marginals, maximum a-posteriori *queries, and *most probable explanations.*