Course Notes
The notes written by students and edited by instructors

Lecture 13: Approximate Inference: Monte Carlo and Sequential Monte Carlo Methods
Wrapping up variational inference, and overview of Monte Carlo methods.

Lecture 11: Kalman Filtering and Topic Models
Kalman Filtering and Topic Models. See abstract. Due to the previous lecture running over, the actual material covered in the lecture deviated from what the lecture schedule suggests.

Lecture 9: Modeling Networks
Classic network learning algorithms.

Lecture 7: Maximum likelihood learning of undirected GM
Algorithms for learning UGMs along with a brief overview of CRFs.

Lecture 6: Learning Partially Observed GM and the EM Algorithm
Introduction to the process of estimating the parameters of graphical models from data using the EM (BaumWelch) algorithm.

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.

Lecture Notes Template
An example of a distillstyle lecture notes that showcases the main elements.