Difference between revisions of "10-601 Introduction to Probability"
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* [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes.pptx Slides in Powerpoint] - '''William'''. | * [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes.pptx Slides in Powerpoint] - '''William'''. | ||
| − | * | + | * [http://www.cs.cmu.edu/~zivbj/classF14/introduction.pdf Slides in pdf] - '''Ziv'''. |
=== Readings === | === Readings === | ||
Revision as of 17:11, 26 August 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
- 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
