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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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Video Length: 3788
Date Found: April 01, 2011
Date Produced: March 31, 2011
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 VideoLectures |
July 10, 2011
The explosion in growth of the Web of Linked Data has provided, for the first time, a plethora of information in disparate locations, yet bound together by machine-readable, semantically typed relations. Utilisation of the Web of Data has been, until now, restricted to members of the community, ...
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 VideoLectures |
July 10, 2011
Problems cannot be solved with the mentality that has caused them’. Hence, the 2008- crisis cannot be solved with ethics of one-sided and short-term mentality of the industrial and neoliberal economics, which has caused the ‘Bubble Economy’ of several recent decades. Neither the market nor the ...
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July 10, 2011
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July 10, 2011
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Social media presents unique challenges for topic classification, including the brevity of posts, the informal nature of conversations, and the frequent reliance on external hyperlinks to give context to a conversation. In this paper we investigate the usefulness of these external hyperlinks ...
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