The course covers advanced topics in machine learning, primarily from Bayesian perspective. A major part of the course is devoted to the graphical models, such as Hidden Markov Models, Bayesian networks, Markov random fields and other methods. The course includes laboratory works in which students get a practical experience of data analysis.
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