MACHINE LEARNING SUMMER SCHOOL 2003

[Manuel Davy]

* Apologises if you receive this email several times *


            CALL FOR PATICIPATION

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       MACHINE LEARNING SUMMER SCHOOL 2003

       August 4-16, 2003, Tuebingen, Germany

      http://www.irccyn.ec-nantes.fr/mlschool/

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Organised by CNRS-AS 66 (France), MPIK Tuebingen (Germany),
and the Australian National University





****         Applications are now open            ****
****  Deadline for application is April 30th 2003 ****
****  For applications, please visit our web site ****



The Summer School is intended for  students and researchers
alike, who are interested in Machine Learning. Its goal is to
present some of the topics which are at the core of modern
Learning Theory. The school will be held in the Max Plank
Institut fuer Biologische Kybernetik, Tuebingen, Germany between
the 4th and the 16th of August, 2003. During this time, we shall
present six basic courses, each one covering one of the
topics listed below. In addition, there will be advance courses
which may focus on additional topics and which will provide
background knowledge in machine learning and statistics. Four
practical sessions will be organized in small groups. The goal
is to provide a 'hands-on' experience of working with machine
learning algorithms. They will be directly related to the material
introduced in the courses.

Basic Courses:
     * 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
     * Kernel Algorithms I
          (A. Smola, ANU) - 6 hours
     * Kernel Algorithms II
          (B. Schoelkopf, MPIK Tuebingen) - 6 hours
     * 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 Approximation
          (L. Bottou, NEC Research, Princeton) - 4 hours
     * Concentration Inequalities
          (S. Boucheron, LRI Orsay) - 4 hours
     * Some Mathematical Tools for Machine Learning
          (C. Burges, Microsoft Research, Redmond) - 4 hours
     * Minimum Description Length
          (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 Approaches, RVM
          (M. Tipping, Microsoft Research, Cambridge) - 4 hours
     * Empirical Inference
          (V. Vapnik) - evening lecture

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

-- 

------------------------------------------
Manuel DAVY
http://www.irccyn.ec-nantes.fr/~davy/
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IRCCyN/CNRS, 1 rue de la Noe,
BP 92101, 44321 Nantes cedex 3, France
Manuel.Davy at irccyn.ec-nantes.fr
Tél: +33 2 40 37 69 09
Fax: +33 2 40 37 69 30
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