Statistical Learning II

Welcome to the course page for Statistical Learning II. This is an old webpage for the Fall 2024 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 2024
  • 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

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

Course Schedule

Date Lecture Topic Materials
Sept 11 - Introduction
- Recap of Linear Algebra
Slides
Sept 18 - Recap of Probability
- Supervised Learning
Slides
Sept 25 - Supervised Learning (continued) Slides
Oct 02 - Least-squares regression Slides
Oct 09 - Least-squares regression (continued) Slides
Oct 16 - Bias-variance decomposition Slides
Oct 23 Midterm exam  
Oct 30 Reading week (no class)  
Nov 06 - Ridge regression Slides
Nov 13 - Ridge regression (continued) Slides
Nov 20 - Best subset selection
- LASSO
Slides
Nov 27 - LASSO (continued) Slides
Dec 04 - PCA
- Feature maps
Slides
Dec 11 - Kernel ridge regression Slides