Schedule
***  Presented in seminar; Assigned as required reading
**  Material alluded to in seminar; highly useful reading
*  Very interesting and/or specialized reading
Meeting Week  Date  Topic  Presenters  Material 

1  W Jan 19 4:306  scheduling mtg  
2  M Jan 24 46  Game Theory  Cathy Ruby / Mike Dinolfo  A Short, Introductory Report on Game Theory  *** [pdf] Non Zero Sum and Nash Equilibrium slides by Andrew Moore  *** [pdf] An Introductory Sketch of Game Theory  *** [html] Computation Game Theory Slides, taught by Michael Kearns  *** [pdf] Interactive Tutorials in Game Theory  ** [htm] Graphical Models for Game Theory  ** [pdf] A Bibliography of Cooperative Games: Value Theory  * [html] 
W Jan 26 46  
3  M Jan 31 46  Monte Carlo  Matt Beal  Introduction to MCMC using chapter 29 of MacKay's book 
W Feb 02 46  Praveen Krishnamurthy*  importance sampling, rejection sampling chapter 29 of MacKay's book 

4  M Feb 07 46  Karthik Sridharan  MetropolisHastings and Gibbs sampling chapter 29 of MacKay's book theory of Markov chains section 3.3 of Neal's technical report Neal, R. M. (1993) Probabilistic Inference Using Markov Chain Monte Carlo Methods, Technical Report CRGTR931, Dept. of Computer Science, University of Toronto, 144 pages 

W Feb 09 46  Praveer Mansukhani  slice sampling, Gibbs sampling, exact sampling chapter 29 of MacKay's book 

5  W Feb 16 2:506  Sankalp Nayak  Hamiltonian Monte Carlo: section 5,0 of Neal's technical report chapter 30 of MacKay's book exact sampling: chapter 32 of Mackay's book O'Hagan: Monte Carlo is fundamentally unsound [pdf] 

6  W Feb 23 2:506  Inference on Graphs  Chetan Bhole  conditional independence relationships, review of Bayesian networks chapter 16 of Mackay's book chapter 1 of Learning in Graphical Models [pdf] 
7  W Mar 02 2:506  Kiran Garaga Lokeswarappa  a little of 1st ch of LIGrModels, intro to BNs, Weiss & Jordan paper, ch 26 mackay (factor graphs) 1st chapter on Learning in Graphical models "Introduction to Inference for Bayesian Networks", Robert Cowell. Murphy reference Weiss & Jordan paper "Graphical Models: Probabilistic Inference", Jordan & Weiss. Following is a list of materials I found useful. First one quotes Michael Jordan in first two paragraphs which I felt is a very good description of Graphical Models. 1) http://www.cs.ubc.ca/~murphyk/Bayes/bayes.html 2) http://www.stanford.edu/~paskin/gmshortcourse/lec3.pdf 3) http://www.cs.wisc.edu/~dpage/cs731/lecture5.ppt#256,1,Junction Trees: Motivation Also CSE 574 slides on graphcial models and time series analysis/. 

8  W Mar 09 2:506  Variational methods  Karthi Bandi  variational methods basics, convex duality, QMRDT An Introduction to Variational Methods for Graphical Models [ps.gz] 
9  W Mar 16 2:506  spring break  
10  W Mar 23 2:506  Yu Wang  block and sequential methods, approximate EM An Introduction to Variational Methods for Graphical Models [ps.gz] 

11  W Mar 30 2:506  Kernel methods / SVMs  Jiang Li  Review of kernel methods, support vector machines, kernel Fisher discriminants, kernel PCA K.R. Mueller, S. Mika, G. Ratsch, K. Tsuda, and B. Schoelkopf. An introduction to kernelbased learning algorithms. [pdf] IEEE Neural Networks, 12(2):181201, May 2001. 
12  W Apr 06 2:506  cancelled  
13  W Apr 13 2:506  Neil Fernandes  Bounds on generalization error O. Bousquet, S. Boucheron, and G. Lugosi Introduction to Statistical Learning Theory. [pdf] G. Lugosi Concentrationofmeasure inequalities. [pdf] 

14  W Apr 20 2:506  PAC Bayes  Matt Beal  The RKHS idea 
15  W Apr 27 2:506       