Introduction to statistical learning
LearningMachine Learning


Undergraduate courses in Analysis and Probability

Objectif du cours

The course presents the mathematical foundations for supervised learning.

Organisation des séances

  • 8 theoretical sessions
  • 3 practical sessions

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Mode de validation

  • Part 1 : partial exam (mandatory)
  • Part 2 : final exam
  • Re-take : written exam


  • M. Mohri, A. Rostamizadeh, A. Talwalkar. Foundations of Machine Learning, The MIT Press, 2012.
  • S. Shalev-Schwartz, S. Ben-David. Understanding Machine Learning: From Theory to Algorithms.Cambridge University Press, 2014.

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Thèmes abordés

  • 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
Les intervenants

Nicolas Vayatis

(Centre Borelli, ENS Paris-Saclay)

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