Difference between revisions of "10-601 Topic Models"
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| − | This a lecture used in the [[Syllabus for Machine Learning 10- | + | This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] |
=== Slides === | === Slides === | ||
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=== Readings === | === Readings === | ||
| + | * Muphy 27.1-27.3 | ||
* LDA is not covered in Mitchell. There's a nice [http://www.cs.princeton.edu/~blei/papers/Blei2012.pdf overview paper on LDA] by David Blei. | * LDA is not covered in Mitchell. There's a nice [http://www.cs.princeton.edu/~blei/papers/Blei2012.pdf overview paper on LDA] by David Blei. | ||
Revision as of 16:55, 6 January 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
- Muphy 27.1-27.3
- LDA is not covered in Mitchell. There's a nice overview paper on LDA by David Blei.
- Here's the code I discussed in class and some sample data.
Summary
You should know:
- what Gibbs sampling is, and how it can be used for inference in a directed graphical model.
- what the graphical models are which are associated with supervised naive Bayes, unsupervised naive Bayes, PLSI, and LDA.
- the relationships between PLSI and matrix factorization.
- how the posterior distribution in a Bayesian model can be used for dimension reduction.
