Difference between revisions of "10-601 Introduction to Probability"
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| − | This a lecture used in the [[Syllabus for Machine Learning 10- | + | This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] |
=== Slides === | === Slides === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes.pptx Slides in Powerpoint] | + | * [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes.pptx Slides in Powerpoint] |
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=== Readings === | === Readings === | ||
Revision as of 16:12, 6 January 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
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
