# Introduction to Probability [Spring 2018]

## References
* [Video Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP60hI9ATjSFgLZpbNJ7myAg6)
* [Course Website](https://ocw.mit.edu/resources/res-6-012-introduction-to-probability-spring-2018/)

## Info
- MIT, John Tsitsiklis

## Content
### Fundamentals
1. Probability Models and Axioms
2. Conditioning and Bayes' Rule
3. Independence
4. Counting
5. Discrete Random Variables
6. Continuous Random Variables
7. Derived Distributions
8. Sum of Independent R.V.s. Covariance and Correlation
9. Conditional Expectation & Variance Revisited; Sum of a Random Number of Independent R.V.s

### Inference and Limit Theorems
1. Introduction to Bayesian Inference
2. Linear Models With Normal Noise
3. Least Mean Squares (LMS) Estimation
4. Linear Least Mean Squares (LLMS) Estimation
5. Inequalities, Convergence, and the Weak Law of Large Numbers
6. The Central Limit Theorem (CLT)
7. An Introduction to Classical Statistics