\newslide{Course outline} \begin{itemize} \item Representation \begin{itemize} \item M Sep 13. Intro \item W Sep 15. Bayes nets (ch 3) \item M Sep 20. Markov nets (ch 5) \item W Sep 22. Markov nets (ch 5); CPDs (ch 4) \end{itemize} \item Exact inference in discrete state-spaces \begin{itemize} \item M Sep 27. Gaussian BNs (ch 4); Intro to inference (ch 6) \item W Sep 29. Variable elimination (ch 7) \item M Oct 4. Variable elimination (ch 7) \item W Oct 6. Junction tree (ch 8) \item M Oct 11. Thanksgiving \item W Oct 13. Guest lecture by Brent Boerlage \item M Oct 18. Belief propagation (ch 8) \end{itemize} \item Learning \begin{itemize} \item W Oct 20. Parameter learning in BNs (ch 12, 13) \item M Oct 25. Bayesian parameter estimation \item W Oct 27. EM (ch 15) \item M Nov 1. Parameter learning in MNs and CRFs (Jordan chaps 9, 19, 20) \item W Nov 3. CRFs \item Mon Nov 8. Bayesian model selection (ch 14, Mackay) \item Wed Nov 10. Structure learning (ch 14) \end{itemize} \item Approximate inference \begin{itemize} \item Mon Nov 15. Project proposals. \item Wed Nov 17. PCA, Kalman filters (J13-15) \item Mon Nov 22. EKF, UKF, ADF \item Wed Nov 24. Sampling/ particle filtering (KF9, J21) \item Mon Nov 29. Variational methods (KF10), EP \item Wed Dec 1. Summary of class. \end{itemize} \end{itemize}