Directed Graphical Models
published: March 31, 2011, recorded: February 2011, views: 4608
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In this talk I introduce the basic concepts of directed graphical models. I then introduce the EM algorithm and discuss learning in latent variable models, considering several mixture models (discrete latent variables), probabilistic PCA (continuous latent variables) and extensions. Next, I describe conditional models for regression, draw links to least squares and ridge regression. Finally, the talk is ended with an introduction to Gaussian process regression.
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