# 10-601 Introduction to Probability

From Cohen Courses

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