Difference between revisions of "10-601 GM3"
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| − | == Slides == | + | |
| + | This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] | ||
| + | |||
| + | === Slides === | ||
* [http://www.cs.cmu.edu/~wcohen/10-601/networks-3-learning.pptx in PPT], [http://www.cs.cmu.edu/~wcohen/10-601/networks-3-learning.pdf in PDF]. | * [http://www.cs.cmu.edu/~wcohen/10-601/networks-3-learning.pptx in PPT], [http://www.cs.cmu.edu/~wcohen/10-601/networks-3-learning.pdf in PDF]. | ||
| + | |||
| + | === Readings === | ||
| + | |||
| + | * See [[10-601 GM1|first lecture on GM]] | ||
| + | * For EM: Mitchell 6.2 or Murphy 11.4.1, 11.4.2, 11.4.4 | ||
| + | |||
| + | === To remember === | ||
| + | |||
| + | * The EM algorithm | ||
| + | ** E-step (expectation step) | ||
| + | ** M-step (maximization step) | ||
| + | * How to use EM to learn DGMs with hidden variables | ||
| + | * How to use EM to learn a mixture of Gaussians | ||
| + | |||
| + | * Connections: | ||
| + | ** naive Bayes as a DGM | ||
| + | ** semi-supervised naive Bayes as a DGM with hidden variables | ||
| + | ** mixture of Gaussians as an a DGM | ||
| + | ** mixture of Gaussians vs k-means | ||
Latest revision as of 11:58, 31 March 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
- See first lecture on GM
- For EM: Mitchell 6.2 or Murphy 11.4.1, 11.4.2, 11.4.4
To remember
- The EM algorithm
- E-step (expectation step)
- M-step (maximization step)
- How to use EM to learn DGMs with hidden variables
- How to use EM to learn a mixture of Gaussians
- Connections:
- naive Bayes as a DGM
- semi-supervised naive Bayes as a DGM with hidden variables
- mixture of Gaussians as an a DGM
- mixture of Gaussians vs k-means
