Current

Intermediate

Probabilistic Graphical Models Specialization

Coursera · Stanford University · Updated March 2026

Platform rating

4.3/5

Champ rating

7.8/10

Duration

4-8 months

Classes

150

Stanford-level course on probabilistic graphical models covering Bayesian networks, Markov random fields, and exact/approximate inference for ML.

What you'll get

Use probability and statistics for ML rigorously

Understand information theory for deep learning

Implement numerical methods for ML algorithms

Course Curriculum

12 chapters

Fit

Best for

AI Researchers

PhD Students

Mathematicians

Frontier Scientists

Not ideal for

Learners seeking only entry-level overviews

Prerequisites & pricing

Prerequisites

Probability and basic ML

Pricing

Subscription

Certification

Certificate

Probabilistic Models

Bayesian Networks

Graphical Models

Inference

Stanford

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