Difference between revisions of "10-601 Introduction to Probability"
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* Inference | * Inference | ||
* Density estimation and classification | * Density estimation and classification | ||
| − | * Naïve Bayes density estimators and classifiers | + | |
| + | * (Ziv - Not sure about this here, why bot leave to the NB class?) Naïve Bayes density estimators and classifiers | ||
* Conditional independence | * Conditional independence | ||
Revision as of 07:37, 12 August 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
- Slides in Powerpoint - William.
- TBD - Ziv.
Readings
- Mitchell Chap 1,2; 6.1-6.3.
What You Should Know Afterward
You should know the definitions of the following, and be able to use them to solve problems:
- Random variables and events
- The Axioms of Probability
- Independence, binomials, multinomials
- Conditional probabilities
- Bayes Rule
- MLE’s, smoothing, and MAPs
- The joint distribution
- Inference
- Density estimation and classification
- (Ziv - Not sure about this here, why bot leave to the NB class?) Naïve Bayes density estimators and classifiers
- Conditional independence
