Difference between revisions of "10-601 Naive Bayes"
From Cohen Courses
Jump to navigationJump to search (→Slides) |
|||
| Line 6: | Line 6: | ||
* I did some examples in Matlab: | * I did some examples in Matlab: | ||
** [[jointDistCommands.m|A joint distribution for a die-rolling problem.]] | ** [[jointDistCommands.m|A joint distribution for a die-rolling problem.]] | ||
| + | ** [[roll.m|The roll subroutine used here - an example of vectorized code.]] | ||
| + | ** [[gaussianJointDistCommands.m|Examples of another joint distribution.]] | ||
=== Readings === | === Readings === | ||
Revision as of 15:41, 9 September 2013
This a lecture used in the Syllabus for Machine Learning 10-601
Slides and Other Materials
- Slides in Powerpoint.
- I did some examples in Matlab:
Readings
- Mitchell 6.1-6.10
What You Should Know Afterward
- How to implement the multinomial Naive Bayes algorithm
- How to interpret the predictions made by the algorithm
