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Accueil > Formations > Master MVA > Présentation des cours

Introduction to statistical learning

Instructor : Nicolas Vayatis (CMLA, ENS Cachan)

Courses objetctives:

The course presents the mathematical foundations for supervised learning.

Topics :

    • Typology of learning problems
    • Statistical models and main algorithms for classification, scoring, ...
    •  Performance criteria and inference principles
    •  Convex risk minimization
    •  Complexity measures
    •  Aggregation and ensemble methods
    •  Main theorems 

      Prerequisites :

      Undergraduate courses in Analysis and Probability

      Organization :

        •   8 theoretical sessions
        •   3 practical sessions

          Validation :

           
            • Part 1 : partial exam (mandatory)
            • Part 2 : final exam or paper review project (report+oral presentation)
            • Re-take : written exam or project (proposed only for students who attended Part 1 and Part 2)

              References :

              • S. Boucheron, O. Bousquet, and G. Lugosi. Theory of Classification: a Survey of Recent Advances. ESAIM: Probability and Statistics, 9:323375, 2005.
              • L. Devroye, L. Györfi, G. Lugosi, A Probabilistic Theory of Pattern Recognition, Springer, New York, 1996.