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Due to the new regulations by the government, the beginning of the course will be fully digital (how long this continues is open atm). My plan is to record and upload video lectures that you can watch when it suits you. If you have any questions about the course, lectures or exercises, don't hesitate to send me an email. The plan is to have lectures on Mondays and exercise sessions on Thursdays (and the videos/material will in general be uploaded on these days). You will find more details in the schedule, which I will update as we go along.

The topic of the Spring 2021 version of STK4290/9290 is Probabilistic Graphical Models (PGMs). The course will give an introduction in the PGM framework, which aim at modelling a system over a large number of variables that interact with each other. The PGM framework lies at the intersection of statistics and computer science, combining concepts from probability theory, graph algorithms and machine learning. We will look at the two most basic PGM representations: Bayesian Networks and Markov networks, and cover the main topics related to PGMs: representation, inference, and learning.

As the main text book for the course, we will use:

Koller, D. and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press. ISBN-13: 978-0262013192, ISBN-10: 0262013193.