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Graphical Models

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If you have a question about this talk, please contact Zoubin Ghahramani.

Machine Learning Tutorial Lecture

This tutorial will provide an introduction to the important field of probabilistic graphical models, which play a central role in the modern view of machine learning. It will cover the main types of graphical model and their factorization and conditional independence properties, and will then explain how inference and learning problems can be solved using local message passing algorithms. The tutorial will assume familiarity with probabilities and Bayes’ theorem, but will not assume any previous knowledge of graphical models.

This talk is part of the Machine Learning @ CUED series.

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