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Reading Stanford CS228 Probabilistic Graphical Models

A week-by-week reading of one explicitly versioned Stanford CS228 offering: probabilistic graphical models, exact and approximate inference, and parameter and structure learning.

aideep-diveReading Stanford CS228 Probabilistic Graphical Models

Probabilistic Graphical Models: Turning Exponential Inference into Polynomial | Stanford CS228 Course Overview

CS228 is Stanford's probabilistic graphical models course, in three parts — representation, inference and learning: graphs write down independence assumptions so that exponentially large probability computations become polynomial. What self-learners can get is the 16-chapter public notes, cs228-notes; lecture slides, recordings and the current assignments are not available.

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