Devavrat Shah: Statistical inference with probabilistic graphical models.

1. Introduction: Graphical models, examples/applications. Key questions: inference (marginals, mode) and learning.

2. Marginal: exact: tree-width; approximation: belief propagation (BP) (sum-product), and it's properties -- fixed point, convergence and approximation/strongly poly-time variation of BP.

3. Mode: exact: tree-width, again; approximation: belief propagation (BP) (max-product).

4. Linear programs, network flows and conditions of optimality. Max-product correctness.

5. Other methods for graphical model inference: variational approximation, partition, sampling/particle filters.

6. Learning Graphical model.