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
| Date | Lecture Topic | Materials |
|---|---|---|
| Sept 09 | - Introduction - Math recap | Slides TD |
| Sept 16 | - Math recap (continued) - Supervised Learning | Slides TD |
| Sept 23 | Supervised Learning (continued) | Slides TD |
| Sept 30 | Least Squares | Slides TD & TP |
| Oct 07 | Least Squares (continued) | Slides TD & TP |
| Oct 14 | Bias-Variance decomposition | Slides Notes |
| Oct 21 | Bias-Variance decomposition (continued) | Slides TD |
| Oct 28 | No class | |
| Nov 04 | Midtern exam | |
| Nov 11 | ||
| Nov 18 | ||
| Nov 25 | ||
| Dec 02 | ||
| Dec 09 |