Course Notes
The notes written by students and edited by instructors

Lecture 8: Causal Discovery and Inference
Learning and inference algorithms for causal discovery.

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 5: Parameter Estimation in Fully Observed Bayesian Networks
Introduction to the problem of Parameter Estimation in fully observed Bayesian Networks

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.

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