Livres
Notes de cours
Ressources de base
More advanced topics:
Appliqué au texte :
Notes de cours
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.