Bayesian Inference: Principles and Practice

author: Mike Tipping, Vector Anomaly
published: Feb. 25, 2007,   recorded: August 2003,   views: 26778


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The aim of this course is two-fold: to convey the basic principles of Bayesian machine learning and to describe a practical implementation framework. Firstly, we will give an introduction to Bayesian approaches, focussing on the advantages of probabilistic modelling, the concept of priors, and the key principle of marginalisation. Secondly, we will exploit these ideas to realise practical algorithms for sparse linear regression and classification, as exemplified by models such as the "relevance vector machine".

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Reviews and comments:

Comment1 ol, July 16, 2007 at 9:35 p.m.:

horrible sound!

Comment2 imer, September 10, 2007 at 6:40 p.m.:

Yeah that sound is really bad.

Comment3 W, January 12, 2008 at 9:23 p.m.:

Someone should check before upload.

Comment4 lola, September 23, 2009 at 5:42 p.m.:

that is perfect

Comment5 ant, September 24, 2009 at 10:28 a.m.:

View it though windows media player then use the graphic equalizer to remove the high frequency hiss.

Comment6 the wowlrus, August 24, 2012 at 7:44 p.m.:

The sound is unintelligible, and the slides are partway off the screen.

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