Course Notes
The notes written by students and edited by instructors

Lecture 4: Exact Inference
Introducing the problem of inference and finding exact solutions to it in graphical models.

Lecture 3: Undirected Graphical Models
An introduction to undirected graphical models

Lecture 2: Bayesian Networks
Overview of Bayesian Networks, their properties, and how they can be helpful to model the joint probability distribution over a set of random variables. Concludes with a summary of relevant sections from the textbook reading.

Lecture 1: Introduction to Graphical Models
Introducing why graphical models are useful, and an overview of the main types of graphical models.