Bayes nets & HMM
Course duration: 8h
This course will give an introduction to the Probability Graphical Models (PGM). PGM is a framework that uses graph-based representation to manipulate, exploit and compactly represent probability distributions.
During introductory part we are going to describe the general representation of PGM, as well as algorithms and machine learning methods to fit the models. The practical session will include few practical cases, where students will learn how to think in terms of graph structures and probability. After the course students should be able to apply PGM using domain knowledge combined with model reasoning. At the end of our course we devote some time to explore the special case of PGM - Hidden Markov Models, where students will be able to build their own computer game.
Tutors
Ms. Anna Leontyeva
Country: Estonia
Place of employment: University of Tartu
Current position: PhD student
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Mr. Dmytro Fishman
Country: Estonia/Ukraine
Place of employment: University of Tartu
Current position: PhD student
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