Course Notes
The notes written by students and edited by instructors
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Lecture 4: Exact Inference
Introducing the problem of inference and finding exact solutions to it in graphical models.
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Lecture 3: Undirected Graphical Models
An introduction to undirected graphical models
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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.
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Lecture 1: Introduction to Graphical Models
Introducing why graphical models are useful, and an overview of the main types of graphical models.