Graphical models are powerful probabilistic modeling tools. They can model the complex behavior of systems of interacting variables through local relations specified using a graph. These probabilistic models represent the conditional dependencies between subsets of variables in a compressed and elegant form. The graphical models' framework has achieved remarkable success across various domains, from near-optimal codes for communication to the state-of-the-art in combinatorial optimization; these models are widely used in bioinformatics, robotics, vision, natural language processing, and machine learning. In this course, we study both directed and undirected models, exact and approximate inference procedures, and learning methods for complete and partial observations.

- Wainwright, Martin J., and Michael I. Jordan. Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 2008
- Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
- Barber, David. Bayesian reasoning and machine learning. Cambridge University Press, 2012.

- Syllabus, review of the probability theory (chapters 2)
- Bayesian Networks (chapter 3)
- Markov Networks (chapter 4)
- Local and Conditional Probability Models (chapter 5)
- Gaussian Network Models (chapter 6)

- Variable Elimination (chapter 7)
- Junction Trees and Belief Propagation (chapter 10)
- Variational Inference (chapters 8, 11, 13)
- Exponential Family and Variational Inference
- Loopy Belief Propagation and Bethe Free Energy
- Naive Mean-Field
- Maximum a Posteriori Inference (chapter 13)
- Sampling Based Inference (chapter 12)
- Monte Carlo Inference in Graphical Models
- Markov Chain Monte Carlo

- Overview: Objectives in Learning chapter 16)
- Maximum likelihood and Bayesian Estimation in Directed Models chapter 17)
- Structure learning in Directed Models (chapter 18)
- Parameter-Learning in Undirected Models (chapter 20)
- Learning with Partial Observations (chapters 19)
- Causality (chapters 21)

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