Difference between revisions of "10-601 Topic Models"
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=== Slides === | === Slides === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-601/ | + | * [http://www.cs.cmu.edu/~wcohen/10-601/lda+sbm.ppt Slides in PowerPoint],[http://www.cs.cmu.edu/~wcohen/10-601/lda+sbm.pdf Slides in PDF]. |
=== Readings === | === Readings === | ||
Revision as of 10:15, 17 November 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
Readings
- LDA is not covered in Mitchell. There's a nice overview paper on LDA by David Blei.
Summary
You should know:
- d-separation, or more generally, how to determine if two variables are conditionally independent in a directed model, and what that means.
- what "explaining away" refers to.
- 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, and LDA.
