Difference between revisions of "10-601 Naive Bayes"
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| − | This a lecture used in the [[Syllabus for Machine Learning 10- | + | This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] |
=== Slides and Other Materials === | === Slides and Other Materials === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-601/ | + | * Catchup - MAP and Joint Distribution: [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes-part2.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes-part2.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 === | ||
* Mitchell 6.1-6.10 | * Mitchell 6.1-6.10 | ||
| + | * Murphy 3 | ||
* [https://code.google.com/p/yagtom/ My favorite on-line Matlab docs] | * [https://code.google.com/p/yagtom/ My favorite on-line Matlab docs] | ||
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
| + | * What conditional independence means | ||
* How to implement the multinomial Naive Bayes algorithm | * How to implement the multinomial Naive Bayes algorithm | ||
| − | * How to interpret the predictions made by the algorithm | + | * How to interpret the predictions made by the NB algorithm |
Latest revision as of 10:03, 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
