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