Difference between revisions of "10-601 Topic Models"
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You should know: | You should know: | ||
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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, PLSI, and LDA. | * 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. | ||
Revision as of 10:18, 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:
- 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.
