Difference between revisions of "10-601 Naive Bayes"
From Cohen Courses
Jump to navigationJump to search| Line 7: | Line 7: | ||
=== Readings === | === Readings === | ||
| − | * | + | * Mitchell 6.1-6.10 |
=== Assignment === | === Assignment === | ||
Revision as of 14:05, 9 September 2013
This a lecture used in the Syllabus for Machine Learning 10-601
Slides
- Slides in Powerpoint. Based on the slides I used for 10-605, they might be updated.
Readings
- Mitchell 6.1-6.10
Assignment
- Implement Naive Bayes and apply it to a couple of datasets. (Details to be posted later.)
What You Should Know Afterward
- How to implement the multinomial Naive Bayes algorithm
- How to interpret the predictions made by the algorithm
