# Difference between revisions of "10-601 Introduction to Probability"

From Cohen Courses

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* 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]. | * 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 | + | ** 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 === |

## 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