Difference between revisions of "10-601 Naive Bayes"
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=== 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 |
Revision as of 10:36, 16 September 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides and Other Materials
- Ziv's lecture: Slides in pdf.
- Slides in Powerpoint.
- I did some examples in Matlab:
Readings
- Mitchell 6.1-6.10
- 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
