Stanford University · Course
Probabilistic Graphical Models: Principles and Techniques (CS228)
Probabilistic Graphical Models: Principles and Techniques (CS228) is a ten-week Stanford Online graduate course surveying graphical models, which combine graph theory and probability to model large collections of random variables with complex interactions.
Who it suits
- Learners who want a rigorous framework for reasoning under uncertainty.
- Researchers in machine learning, vision, language processing or computational biology.
What it covers
- The key formalisms for representing complex domains with probability distributions.
- Techniques to construct models and make predictions.
- Using graphical models to support decision making under uncertainty.
Before you start
- Stanford lists a conferred bachelor’s degree with an undergraduate GPA of 3.0 or better.
Common questions about Probabilistic Graphical Models: Principles and Techniques (CS228)
- How long is it?
- Stanford lists 10 weeks at 15 to 25 hours a week, for 4 academic units.
- Is it open for enrolment?
- Stanford’s page said it was not open when we read it; check the page.
- Which certificates count it?
- Stanford lists the Robotics and Autonomous Systems, Artificial Intelligence and Electrical Engineering graduate certificates.
More like this
Keep learning on Infrato
- Path: AI for data analysts
- Path: AI for software developers
- More from Stanford University: all Stanford University listings in the directory
- Explore all AI courses
Where this comes from
Written from Stanford University’s own pages, read on 2026-10-14. Content, prices and availability change, so the provider’s page is the source. We are not affiliated with Stanford University and this is not an endorsement.