High Dimensional Statistical Learning (HDL) - Master 2 SIF


This module provides a detailed overview of the mathematical foundations of modern statistical learning by describing the theoretical basis and the conceptual tools needed to analyze and justify the algorithms. The emphasis is on problems involving high volumes of high dimensional datasets, and on dimension reduction techniques allowing to tackle them. The course involves detailed proofs of the main results and associated exercices.


PAC (probably approximately correct), random projection, PCA (principal component analysis), concentration inequalities, measures of statistical complexity.


The prerequisites for this course include previous coursework in linear algebra, multivariate calculus, basic probability (continuous and discrete) and statistics. Previous coursework in convex analysis, information theory, and optimization theory would be helpful but is not required. Students are expected to be able to follow a rigorous proof.


Acquired skills


Aline Roumy (responsible), Adrien Saumard, Maël Le Treust (invited teacher).

Course schedule (2021-2022)

The course is scheduled (check detailed times and rooms on ENT (click ISTIC>M2 SIF)) Detailed schedule


Modalities: read carefully the recommendation in (PDF)
Dates and link to the documents:

Some references

Course Notes