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] |
| + | * [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes.pdf Slides in PDF] | ||
=== Readings === | === Readings === | ||
* Mitchell Chap 1,2; 6.1-6.3. | * Mitchell Chap 1,2; 6.1-6.3. | ||
| + | * Optional: [http://www.cs.cmu.edu/~tom/mlbook/Joint_MLE_MAP.pdf Draft of Chapter 2 of Tom's new textbook]. | ||
| + | ** If you find an error in this, email Tom - a reward is offered for bug-finders. | ||
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
| Line 16: | Line 19: | ||
* 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 | * The joint distribution | ||
| − | * | + | * How to do inference using the joint distribution |
* Density estimation and classification | * Density estimation and classification | ||
| − | |||
| − | |||
Latest revision as of 16:38, 15 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.
- Optional: Draft of Chapter 2 of Tom's new textbook.
- If you find an error in this, email Tom - a reward is offered for bug-finders.
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
