Research Resources

Bayesian Networks, Probabilistic Models

• A Tutorial on Learning With Bayesian Networks, David Heckerman. A good starting point for BN
• Probabilistic Models for Unsupervised Learning, Tutorial presented at the 1999 NIPS Conference by Zoubin Ghahramani and Sam Roweis
• Tutorial on Graphical Models and Bayesian Networks, Kevin Murphy

Boosting

• www.boosting.org
• Additive Logistic Regression: a Statistical View of Boosting, Jerome Friedman, Trevor Hastie, & Robert Tibshirani.

Collaborative and Content-Based Information Filtering

• Collaborative Filtering Mailing List Archive By Thread

Kernel Methods

• www.Kernel-Machines.org,  Bernhard Schölkopf
• http://www.rpi.edu/~bennek/kpbsas.ppt, very interesting slides on SVM,  by Kristin P. Bennett

Latex

• Math symbols  Math   General Help   Tutorial   Latex 101

Manifold Learning

• Martin Law's Manifold Learning Resource Page. Cool!

Nonparametric/Hierarchical/Empirical Bayesian

• Nonparametric Bayesian Methods and Infinite Models, NIP*03 workshop

Applied Mathematics

• Book Convex Optimization, by Stephen Boyd and Lieven Vandenberghe

Gaussian Process

• Gaussian Process for Regression and Classification web page, at Univ. of Toronto
• Gaussian Process web page, David Mackay
• An Introduction to Gaussian Process, David Mackay

General Statistical Machine Learning

• Hastie et al, the elements of statistical learning: data mining, inference, and prediction. A very good book. some interesting data sets.
• Hastie and Tibshirani 1995, Generalized Additive Models.
• Sam Roweis's research notes on machine learning.
• Tom Minka's statistical learning / pattern recognition glossary, it covers brief introductions to a wide range of concepts, and more importantly, provides corresponding major references.
• How to do the research at MIT AI lab. pretty old but still something useful to my research.
• Image, World and Knowledge, by Yingnian Wu. An interesting discussion on why prefer statistical models, especially generative models. "Learning is fundamentally unsupervised!"

Text and Web Processing

• Book: Modeling the Internet and the Web, Probabilistic Methods and Algorithms, Pierre Baldi, Paolo Frasconi, Padhraic Smyth.
• Martin Porter's stemming algorithm
• Online Book: Information Retrieval, by C. J. van RIJSBERGEN

Variational Bayesian Inference

• Variational-Bayes repository maintained by Matt Beal and Zoubin Ghahramani
• An Introduction to Variational Methods for Graphical Methods, Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul.