Difference between revisions of "10-601 Topic Models"
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| − | * | + | * The relationships between PLSI and matrix factorization. |
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* what Gibbs sampling is, and how it can be used for inference in a directed graphical model. | * 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. | + | * what the graphical models are which are associated with supervised naive Bayes, unsupervised naive Bayes, PLSI, and LDA. |
Revision as of 10:17, 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:
- The relationships between PLSI and matrix factorization.
- 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.
