Difference between revisions of "10-601 Naive Bayes"
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== Assignment == | == Assignment == | ||
| − | * Implement Naive Bayes. (Details to be posted later.) | + | * Implement Naive Bayes and apply it to a couple of datasets. (Details to be posted later.) |
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
Revision as of 09:15, 3 July 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
- None
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
