Difference between revisions of "10-601 Introduction to Probability"
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=== Readings === | === Readings === | ||
| − | * | + | * Mitchell Chap 1,2; 6.1-6.3. |
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
Revision as of 09:44, 16 August 2013
This a lecture used in the Syllabus for Machine Learning 10-601
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
- 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
