Statistical Learning II

Welcome to the course page for Statistical Learning II. Here you’ll find all the course information and materials.

Course Information

• Instructors:
  • Dr. Bruno Loureiro (main instructor)
  • Alejandro Saldarriaga Fuertes (TA)
• Email: bruno.loureiro@di.ens.fr
• Semester: Fall 2026
• 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 2nd year (L2) 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