Difference between revisions of "10-601 Introduction to Probability"
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* Bayes Rule | * Bayes Rule | ||
* MLE’s, smoothing, and MAPs | * MLE’s, smoothing, and MAPs | ||
| − | * | + | * Inference using the joint distribution |
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* Density estimation and classification | * Density estimation and classification | ||
Revision as of 10:29, 16 September 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
- Slides in Powerpoint - William.
- Slides in pdf - 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
- Inference using the joint distribution
- Density estimation and classification
