Statistical learning with extremes,
Master MVA, université Paris-Saclay

Joint course with Stephan Clemencon, Antoine Doize.

Syllabus

Location/Schedule in 2024: at Telecom Paris (Palaiseau), room 1D23, on Thursdays, 9:30--12:30.

Registration (mandatory)

Course material

• lecture notes (last update: 2024/10/23)

• slides lecture 1, (2024/10/03)

• Lab 1 and partial correction (up to question 2.1)

• Lab2

Homework

• Lecture 1 (2024/10/03): Exercise 1.3 (Erratum: Question 3: the expression `to the power alpha' should be 'to the power minus alpha'. This has been corrected in the latest version)

• Lecture 2 (2024/10/10): Section 3 from Lab1.

• Lecture 3 (2024/10/17):two exercises out of four among Exercises 2.4, 2.5, 2.6, 2.7 (no code. scanned handwritten, readable notes allowed)

• Lecture 4 (2024/10/24): Lab 3 (warning: assignment has been modified (typo + one more question) on 2024/10/29)

Recommended reading

The second part of this course focuses on statistical learning theory for extreme value analysis. It is highly recommended to familiarize yourself with basic statistical learning theory. Good starting points are the book Foundations of Machine Learning (Mohri et al.), Chapters 2,3; or G. Lugosi's lecture notes on the topic of binary classification. The first four sections are particularly relevant to our context. N.B. These lecture notes are part of the book `Principles of Nonparametric Learning' .

Instructions for oral presentations and report, list of articles

• Instructions