Statistical Learning II

Welcome to the course page for Statistical Learning II. This is an old webpage for the Fall 2025 iteration of the course. Visit this page the most recent iteration with the course material.

Course Information

• Instructors:
  • Dr. Bruno Loureiro (main instructor)
  • Leonardo Defilippis (TA)
• Email: bruno.loureiro@di.ens.fr
• Semester: Fall 2025
• Class Times: Wednesdays, 08:30 AM - 11:45 AM
• Location: Check at the Dauphine ENT.

Course Description

This course is a continuation of the “Statistical Learning I” course taught at the 3rd year (L3) of the Double Licence Intelligence Artificielle et Sciences des Organisations (IASO) undergraduate from Université Paris-Dauphine.

Our goal is to build a basic understanding of the mathematics behind some of the classical machine learning algorithms, such as:

• Least squares regression
• Ridge regression
• LASSO
• PCA
• Kernel methods

Recommended literature

The material in this course takes inspiration from the following excellent ressources:

• Bach, Francis. Learning theory from first principles. MIT press, 2024.
• Hastie, Trevor, et al. The elements of statistical learning: data mining, inference, and prediction. Vol. 2. New York: springer, 2009.
• Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
• Wasserman, Larry. All of statistics: a concise course in statistical inference. Springer Science & Business Media, 2013.

Course Schedule

For the course material, refer to the most recent iteration of the course.