Prè-requis
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
Mode de validation
- Part 1 : partial exam (mandatory)
- Part 2 : final exam
- Re-take : written exam
Références
- 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.
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)
