10-708: Probabilistic Graphical Models

Carnegie Mellon Machine Learning Department, Spring 2025

Instructors: Prof. Andrej Risteski and Prof. Albert Gu

Key Topics

  • Undirected graphical models (Markov Random Fields)
  • Directed graphical models (Bayesian networks)
  • Learning and inference using variational methods
  • Sampling using Markov Chain Monte Carlo techniques
  • Deep Generative Models (RBMs, VAEs, GANs, diffusion models)
  • Topics in causality and graph neural networks