Difference between revisions of "10-601 Naive Bayes"
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=== Slides and Other Materials === | === Slides and Other Materials === | ||
| − | * | + | |
| + | * Catchup - MAP and Joint Distribution: [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes-part-2.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes-part-2.pdf Slides in PDF] | ||
| + | * Main lecture: [http://www.cs.cmu.edu/~wcohen/10-601/nb.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/nb.pdf Slides in PDF] | ||
=== Readings === | === Readings === | ||
Revision as of 11:02, 20 January 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides and Other Materials
- Catchup - MAP and Joint Distribution: Slides in Powerpoint, Slides in PDF
- Main lecture: Slides in Powerpoint, Slides in PDF
Readings
- Mitchell 6.1-6.10
- Murphy 3
- My favorite on-line Matlab docs
What You Should Know Afterward
- What conditional independence means
- How to implement the multinomial Naive Bayes algorithm
- How to interpret the predictions made by the NB algorithm
