tutorials:gmm.html

**Gaussian Mixture Models (GMMs) are among the most statistically mature
methods for clustering (though they are also used intensively for
density estimation). In this tutorial, we introduce the concept of
clustering, and see how one form of clustering…in which we assume that
individual datapoints are generated by first choosing one of a set of
multivariate Gaussians and then sampling from them…can be a
well-defined computational operation. We then see how to learn such a
thing from data, and we discover that an optimization approach not used
in any of the previous Andrew Tutorials can help considerably here. This
optimization method is called Expectation Maximization (EM). We'll spend
some time giving a few high level explanations and demonstrations of EM,
which turns out to be valuable for many other algorithms beyond Gaussian
Mixture Models (we'll meet EM again in the later Andrew Tutorial on
Hidden Markov Models). The wild'n'crazy algebra mentioned in the text
can be found (hand-written)
Download Tutorial Slides (PDF format).**

Download Tutorial Slides (PDF format)

**Powerpoint Format:** The Powerpoint originals of these slides are
freely available to anyone who wishes to use them for their own work, or
who wishes to teach using them in an academic institution. Please email
Andrew Moore at awm@cs.cmu.edu if you would
like him to send them to you. The only restriction is that they are not
freely available for use as teaching materials in classes or tutorials
outside degree-granting academic institutions.

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tutorials/gmm.html.txt · Last modified: 2016/10/04 21:27 (external edit)

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