Winter 2023

Stanford University

An in-depth study of probabilistic graphical models, combining graph and probability theory. Equips students with the skills to design, implement, and apply these models to solve real-world problems. Discusses Bayesian networks, exact and approximate inference methods, etc.

Probabilistic graphical models are a powerful framework for representing complex domains using probability distributions, with numerous applications in machine learning, computer vision, natural language processing and computational biology. Graphical models bring together graph theory and probability theory, and provide a flexible framework for modeling large collections of random variables with complex interactions. This course will provide a comprehensive survey of the topic, introducing the key formalisms and main techniques used to construct them, make predictions, and support decision-making under uncertainty.

The aim of this course is to develop the knowledge and skills necessary to design, implement and apply these models to solve real problems. The course will cover: (1) Bayesian networks, undirected graphical models and their temporal extensions; (2) exact and approximate inference methods; (3) estimation of the parameters and the structure of graphical models.

Students are expected to have background in basic probability theory, statistics, programming, algorithm design and analysis. If you are able to comfortably able to complete homework 1 then you likely have all the relevant background knowledge.

No data.

**Corresponding Textbook**: (“PGM”) *Probabilistic Graphical Models: Principles and Techniques* by Daphne Koller and Nir Friedman. MIT Press.

**Course Notes**: Available here. Student contributions welcome!

**Lecture Videos**: here

**Further Readings**:

- (“GEV”)
*Graphical models, exponential families, and variational inference*by Martin J. Wainwright and Michael I. Jordan. Available online. *Modeling and Reasoning with Bayesian Networks*by Adnan Darwiche. Available online (through Stanford).*Pattern Recognition and Machine Learning*by Chris Bishop. Available online.*Machine Learning: A Probabilistic Perspective*by Kevin P. Murphy. Available online (through Stanford).*Information Theory, Inference, and Learning Algorithms*by David J. C. Mackay. Available online.*Bayesian Reasoning and Machine Learning*by David Barber. Available online.

Course notes available at notes

No videos available

No assignements available

Other resources available at Other Resources