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1. Basic Probability and Statistics
2. Introduction to Graphical Models
3. Simple Graphical Models: Linear Classification and Regression
4. Parameter Estimation for Completely Observed Graphical Models
5. Expectation Maximization: Parameter Estimation for Incomplete Graphical Models
6. Exact Inference on Graphical Models
7. Multivariate Guassians
8. Factor Analysis
9. Hidden Markov Model
10. Kalman Filtering and Applications in Tracking
11. Approximate Inference on Graphical Models
12. Model selection and Decision Theory
13. VC Theory
14. Support Vector Machines