Machine Learning Summer School 2003

Topics

• Statistical Learning Theory (O. Bousquet, MPIK Tuebingen) - 8 hours
• Independent Component Analysis (J.-F. Cardoso, ENST Paris) - 8 hours
• Gaussian Processes (C. Rasmussen, MPIK Tuebingen) - 8 hours
• Learning with Kernels (B. Schoelkopf, MPIK Tuebingen) - 6 hours
• Unsupervised Learning with Kernels (A. Smola, ANU) - 6 hours
• An Introduction to Pattern Classification (E. Yom-Tov, Technion, Haifa) - 4 hours

Advanced Courses:

• Monte Carlo Simulation methods (C. Andrieu, University of Bristol) - 4 hours
• Bioinformatics (P. Baldi, UC Irvine) - 4 hours
• Stochastic Learning (L. Bottou, NEC Research, Princeton) - 4 hours
• Concentration Inequalities with Machine Learning Applications (S. Boucheron, LRI Orsay) - 4 hours
• Some Mathematical Tools for Machine Learning (C. Burges, Microsoft Research, Redmond) - 4 hours
• Universal Modeling: Introduction to modern MDL (P. Grunwald, CWI Amsterdam) - 4 hours
• Information Retrieval and Language Technology (T. Joachims, Cornell University) - 4 hours
• Foundations of Learning (S. Smale, UC Berkeley) - 4 hours
• Bayesian Inference: Principles and Practice (M. Tipping, Microsoft Research, Cambridge) - 4 hours

Evening talks

• Empirical Inference (V. Vapnik)
• Analysis of Support Vector Machine Classification (D.-X. Zhou)
• On Learning Vector-Valued Functions (M. Pontil)

Practical sessions:

• Pattern classification - From data to decision (E. Yom-Tov)
• Support Vector Machines (A. Gretton, A. Elisseeff, J. Weston)
• Simulation Methods (Manuel Davy)

Fancy Graphics

Organizers