published: Aug. 5, 2010, recorded: July 2010, views: 685
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We will discuss probabilistic graphical models associated to directed and undirected graphs. We will introduce exact inference algorithms, such as the sum-product algorithm, and apply it to hidden Markov models. We will also discuss elements of learning in graphical models including maximum likelihood, maximum a posteriori and the expectation-maximisation algorithm.
Download slides: bootcamp2010_archambeau_gm_01.pdf (1.1 MB)
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