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Last update: T_KTI (03.05.2012)
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Last update: Mgr. Marta Vomlelová, Ph.D. (14.05.2021)
The course gives an introduction to probabilistic graphical models. The students will learn the following formal models, evaluation and model learning algorithm, application areas. |
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Last update: Mgr. Marta Vomlelová, Ph.D. (07.06.2019)
The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus. |
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Last update: Mgr. Marta Vomlelová, Ph.D. (20.04.2016)
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Last update: Mgr. Marta Vomlelová, Ph.D. (07.06.2019)
The exam consists of a written preparation and an oral part. The requirements are given by the course syllabus. |
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Last update: Mgr. Marta Vomlelová, Ph.D. (09.05.2023)
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. |