Extra Readings

[AFDJ03]	An introduction to MCMC for machine learning
	by C. Andrieu, N. de Freitas, A. Doucet and M. I. Jordan.
	Machine Learning, 2003.
[B98]	A Tutorial on Support Vector Machines for Pattern Recognition
	by Chris Burges.
	KDDM, 1998.
[BBBCL07]	Robust Reductions from Ranking to Classification
	by Nina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, and Greg Sorkin.
	COLT 2007.
[BDHLZ05]	Reductions Between Classification Tasks
	by Alina Beygelzimer, Varsha Dani, Tom Hayes, John Langford and Bianca Zadronzny.
	ICML, 2005.
[BKNS04]	Policy search by dynamic programming
	by J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng and Jeff Schneider.
	NIPS 2004.
[BNJ03]	Latent Dirichlet allocation
	by Dave Blei, Andrew Ng and Michael Jordan.
	JMLR, 2003. (You can ignore Section 5 (Inference and Parameter Estimation)).
[GE03]	An Introduction to Variable and Feature Selection
	by Isabelle Guyon and Andre Elisseeff.
	JMLR 2003.
[GS04]	Finding scientific topics
	by Tom Griffiths and Mark Steyvers.
	PNAS, 2004.
[J04]	Graphical models
	by Michael I. Jordan.
	Statistical Science 2004.
[KKJ03]	Exploration in Metric State Spaces
	by Sham Kakade, Michael Kearns, and John Langford.
	ICML 2003.
[KSD06]	Learning Low-Rank Kernel Matrices
	by Brian Kulis, Matyas Sustik, Inderjit Dhillon.
	ICML 2006.
[L03]	Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
	by Neil Lawrence.
	NIPS 2003.
[L05]	Tutorial on Practical Prediction Theory for Classification
	by John Langford.
	JMLR 2005.
[M03]	Simplified PAC-Bayesian Margin Bounds
	by David McAllester.
	COLT 2003.
[MPKWJW05]	Simple Algorithms for Complex Relation Extraction with Applications to Biomedical IE
	by R. McDonald, F. Pereira, S. Kulick, S. Winters, Y. Jin, and P. White.
	ACL 2005.
[N06]	Linear algebra review and reference
	by Andrew Ng.
	Draft tutorial, 2006.
[NG00]	PEGASUS: A policy search method for large MDPs and POMDPs
	by Andrew Y. Ng and Michael I. Jordan.
	UAI 2000.
[NMM06]	Semi-supervised Text Classification Using EM
	by Kamal Nigam, Andrew McCallum and Tom Mitchell.
	In Semi-supervised Learning, 2006.
[PS07]	Policy Gradient Methods for Robotics
	by Jan Peters and Stefan Schaal.
	IROS 2006.
[Q86]	Induction of Decision Trees
	by J.R. Quinlan.
	MLJ, 1986.
[S99]	Perceptron, Winnow, and PAC Learning
	by R. Servedio.
	COLT 1999.
[SB98]	Reinforcement Learning: An Introduction
	by Rich Sutton and Andrew Barto.
	MIT Press, 1998.
[SM06]	An Introduction to Condition Random Fields for Relational Learning
	by Charles Sutton and Andrew McCallum.
	Book Chapter in Introduction to Statistical Relational Learning, 2006.
[SWHSL06]	Spectral methods for dimensionality reduction
	by L. Saul, K. Weinberger, J. Ham, F. Sha and D. Lee.
	In "Semisupervised learning" 2006.
[T08]	Dirichlet Processes
	by Yee Whye Teh.
	Draft tutorial, 2008.
[TDR07]	Bayesian Agglomerative Clustering with Coalescents
	by Yee Whye Teh, Hal Daumé III and Daniel Roy
	NIPS 2007.
[WBS06]	Distance Metric Learning for Large Margin Nearest Neighbor Classification
	by Kilian Weinberger, John Blitzer and Lawrence Saul.
	NIPS 2006.
[WSZS07]	Graph Laplacian methods for large-scale semidefinite programming, with an application to sensor localization
	by Kilian Weinberger, Fei Sha, Qihui Zhu and Lawrence Saul.
	NIPS 2007.
[ZGL03]	Semi-supervised learning using Gaussian fields and harmonic functions
	by Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty.
	ICML 2003.