Syllabus
1) A brief refresh of the Artificial Intelligence 2 course, Causal and Bayesian networks,
2) advanced evaluation methods: d-separation, junction tree, message passing scheme,
3) dynamic Bayesian networks DBNs,
4) learning Bayesian networks,
5) decision graphs,
6) POMDP - partially observable Markov decision problems,
7) variational approximate inference
8) example applications. Basic introduction into the Python libraries pgmpy, bayespy.
Annotation
The course extends the basics of probabilistic graphical models introduced in the NAIL070
Artificial Intelligence 2 course: Bayesian networks and their extensions (DBN, OOBN), decision graphs, partially observable markov decision processes (POMDP) and conditional random fields.
We focus on the modelling languages and their evaluation methods. We touch also some applications.