Difference between revisions of "10-601 Introduction to Probability"
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* The Axioms of Probability | * The Axioms of Probability | ||
* Independence, binomials, multinomials | * Independence, binomials, multinomials | ||
| + | * Expectation and variance of a distribution | ||
* Conditional probabilities | * Conditional probabilities | ||
* Bayes Rule | * Bayes Rule | ||
* MLE’s, smoothing, and MAPs | * MLE’s, smoothing, and MAPs | ||
| − | * | + | * The joint distribution |
| + | * How to do inference using the joint distribution | ||
* Density estimation and classification | * Density estimation and classification | ||
Revision as of 09:32, 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
- Expectation and variance of a distribution
- Conditional probabilities
- Bayes Rule
- MLE’s, smoothing, and MAPs
- The joint distribution
- How to do inference using the joint distribution
- Density estimation and classification
