Difference between revisions of "10-601 Introduction to Probability"
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=== 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] - '''William'''. |
| + | * TBD - '''Ziv'''. | ||
=== Readings === | === Readings === | ||
Revision as of 15:02, 11 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
- Naïve Bayes density estimators and classifiers
- Conditional independence
