Probabilistic Graphical Models Specialization By Stanford University Course Syllabus

Overview: This specialization provides a comprehensive introduction to probabilistic graphical models (PGMs), covering representation, inference, and learning. Divided into three core courses, it spans approximately 170 hours of learning, combining theoretical foundations with hands-on programming assignments. You'll explore Bayesian networks, Markov networks, exact and approximate inference methods, parameter estimation, and structure learning, culminating in real-world applications across domains like healthcare and AI.

Module 1: Probabilistic Graphical Models 1: Representation

Estimated time: 66 hours

• Introduction to Bayesian Networks (directed graphical models)
• Representation and semantics of Markov Networks (undirected models)
• Conditional independence and factorization in PGMs
• Constructing graphical models for real-world problems

Module 2: Probabilistic Graphical Models 2: Inference

Estimated time: 38 hours

• Exact inference using variable elimination
• Belief propagation and sum-product algorithm
• Approximate inference with Markov Chain Monte Carlo (MCMC)
• Implementing inference algorithms in practice

Module 3: Probabilistic Graphical Models 3: Learning

Estimated time: 66 hours

• Parameter estimation in Bayesian and Markov networks
• Structure learning from data
• Expectation-Maximization (EM) algorithm and its applications
• Applying learning algorithms to real datasets

Module 4: Bayesian Networks and Directed Models

Estimated time: 20 hours

• D-separation and independence properties
• Bayesian network construction and parameterization
• Efficient representation using CPDs (Conditional Probability Distributions)

Module 5: Markov Networks and Undirected Models

Estimated time: 20 hours

• Factor graphs and Gibbs distributions
• Independence properties in undirected models
• Applications in image analysis and spatial modeling

Module 6: Final Project

Estimated time: 30 hours

• Design a PGM for a real-world problem (e.g., medical diagnosis)
• Implement inference and learning components
• Submit a report analyzing model performance and insights

Prerequisites

• Strong background in probability theory
• Familiarity with statistics and linear algebra
• Basic programming skills for assignments

What You'll Be Able to Do After

• Understand and apply Bayesian and Markov networks
• Perform exact and approximate inference in PGMs
• Estimate parameters and learn model structure from data
• Apply PGMs to domains like healthcare, NLP, and computer vision
• Implement core PGM algorithms in practical settings