Difference between revisions of "10-601 Topic Models"
From Cohen Courses
Jump to navigationJump to search| Line 9: | Line 9: | ||
* 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. | ||
| − | * | + | * Here's the [http://www.cs.cmu.edu/~wcohen/10-601/lda-demo code I discussed in class] and some sample data. |
=== Summary === | === Summary === | ||
Revision as of 16:52, 18 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.
- 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.
