# Probabilistic Models

## Review of Probability and Inference {#sec-reviewprobability}
This material should have been covered in Stochastics and Machine Learning, so this is largely a review.

### Joint/Conditional Probability and Bayes Rule

### Maximum Likelihood Estimation

### Maximum Aposteriori Estimation

### Example: Bayesian Linear Regression


## Introduction to Probabilistics Graphical Models

## Approximate Inference

## The Exponential Family

## Exercises

### Derving the MLE for Various Common Distributions