Difference between revisions of "10-601 Naive Bayes"
From Cohen Courses
Jump to navigationJump to search| Line 3: | Line 3: | ||
=== Slides and Other Materials === | === Slides and Other Materials === | ||
* William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/nb.pptx Slides in Powerpoint] | * William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/nb.pptx Slides in Powerpoint] | ||
| − | |||
| − | |||
| − | |||
| − | |||
=== Readings === | === Readings === | ||
Revision as of 18:03, 19 January 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides and Other Materials
- William's lecture: Slides in Powerpoint
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
