Livres

• Hastie-Tibshirani

Notes de cours

• Max Welling

Ressources de base

• Linear Algebra Review and Reference by Andrew Ng.
• Maxent Tutorial by Adam Berger
• A Tutorial on Support Vector Machines for Pattern Recognition by Chris Burges, KDD 98.
• Induction of Decision Trees by J.R. Quinlan, MLJ 86.
• Reductions Between Classification Tasks by Alina Beygelzimer, Varsha Dani, Tom Hayes, John Langford and Bianca Zadronzny, ICML 2005.
• A brief introduction to boosting by Robert Schapire, IJCAI 1999
• An Introduction to Variable and Feature Selection by Isabelle Guyon and Andre Elisseeff, JMLR 2003.
• Using EM To Estimate A Probablity Density With A Mixture Of Gaussians by Aaron D'Souza

More advanced topics:

• The Expectation Maximization Algorithm: A short tutorial by Sean Borman.
• Advances in Gaussian Processes by Carl Edward Rasmussen, NIPS 2006

Appliqué au texte :

• Semi-supervised Text Classification Using EM by Kamal Nigam, Andrew McCallum and Tom Mitchell. In Semi-supervised Learning, 2006.
• Maximum Entropy Markov Models for Information Extraction and Segmentation by Andrew McCallum, Dayne Freitag and Fernando Pereira, ICML, 2000.
• Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms by Michael Collins. EMNLP 2002.
• Latent Dirichlet allocation by Dave Blei, Andrew Ng and Michael Jordan.JMLR 2003.
• Finding scientific topics by Tom Griffiths and Mark Steyvers.PNAS 2004. Application du précédent.