Statistical Learning Theory

Data sets: A collection of datasets in LIBSVM format; Some digits, faces, text datasets in MATLAB format provided by Professor Roweis; See the FAQ of LIBSVM about converting data formats
Software: Netlab neural network software; LIBSVM: A library for Support Vector Machines; LIBSVM Tools; LIBLINEAR: A library for large linear classification; JBoost: An implementation of boosting in java
Related Courses and Video Lectures: Stanford CS229 Machine Learning: the course materials are helpful; Professor Andrew Ng's lecture videos are available on YouTube.; CMU 10-601 Machine Learning; Machine Learning taught by Professor Greg Mori; his lecture slides are based on PRML (Bishop); Professor Daume's Machine Learning Courses 2009 and 2008 at the University of Utah; PRML Reading Group at INRIA: slides for PRML chapters 1-13 are available; Machine Learning Summer Schools; Pascal Lecture Series; CMU Machine Learning Lunch Seminar
Conferences and Journals: NIPS 2009 papers; ICML 2009; NIPS 2008 papers; ICML 2008 papers; Journal of Machine Learning Research (JMLR)
Related Papers and Books: Convex Optimization by Boyd and Vandenberghe; In Defense of Nearest-Neighbor Based Image Classification, Boiman et al., CVPR 2008.