cse 714




*** - Presented in seminar; Assigned as required reading
  ** - Material alluded to in seminar; highly useful reading
    * - Very interesting and/or specialized reading

Meeting WeekDate Topic Presenters Material
1W Jan 19 4:30-6 scheduling mtg    
2 M Jan 24 4-6 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 4-6
3 M Jan 31 4-6 Monte Carlo Matt Beal
Introduction to MCMC
  using chapter 29 of MacKay's book
W Feb 02 4-6 Praveen Krishnamurthy*
importance sampling, rejection sampling
  chapter 29 of MacKay's book
4 M Feb 07 4-6 Karthik Sridharan
Metropolis-Hastings 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 CRG-TR-93-1, Dept. of Computer Science, University of Toronto, 144 pages
W Feb 09 4-6 Praveer Mansukhani
slice sampling, Gibbs sampling, exact sampling
  chapter 29 of MacKay's book
5 W Feb 16 2:50-6 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 2:50-6 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 2:50-6 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/gm-short-course/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 2:50-6 Variational methods Karthi Bandi
variational methods basics, convex duality, QMR-DT
 An Introduction to Variational Methods for Graphical Models [ps.gz]
9 W Mar 16 2:50-6 spring break  
10 W Mar 23 2:50-6 Yu Wang
block and sequential methods, approximate EM
 An Introduction to Variational Methods for Graphical Models [ps.gz]
11 W Mar 30 2:50-6 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 kernel-based learning algorithms. [pdf]
	IEEE Neural Networks, 12(2):181-201, May 2001.
12 W Apr 06 2:50-6 cancelled  
13 W Apr 13 2:50-6 Neil Fernandes
Bounds on generalization error
	O. Bousquet, S. Boucheron, and G. Lugosi
	Introduction to Statistical Learning Theory. [pdf]
	G. Lugosi
	Concentration-of-measure inequalities. [pdf]
14 W Apr 20 2:50-6 PAC Bayes Matt Beal
The RKHS idea
15 W Apr 27 2:50-6 - -