Latest

2025.01.17: Updated assignment with details about computing the SVD.
2025.01.13: Homework assignment.
2025.01.06: Solutions tutorials+labs.
2024.11.12: Board lecture 10+lab 2.
2024.11.05: Board lecture 9.
2024.11.03: Update tutorial 4.
2024.10.29: Tutorial 4+board lecture 8.
2024.10.15: Update following lecture 7.
2024.10.12: Update following lecture 6+lecture notes and tutorials 2/3.
2024.10.01: Update following lecture 5+lab session.
2024.09.24: Update following lecture 4.
2024.09.17: Update following lecture 3.
2024.09.10: Update following lecture 2.
2024.09.09: First exercise sheet+chapter 1 lecture notes.
2024.09.03: Board from course 1 online.
2024.09.03: The course webpage is online.

Instructors

Thibault De Surrel De Saint Julien
thibault.de-surrel@lamsade.dauphine.fr

Clément Royer (Head of the course)
clement.royer@lamsade.dauphine.fr

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Fundamentals of Machine Learning

L3 IM2D, Université Paris Dauphine-PSL, 2024-2025

Goals for this course

Present standard data analysis techniques based on linear models.

Homework (deadline: February 7)

Assignment (in French, current version: Jan. 17) PDF

Course material (in French)

     Lecture notes (updated Nov. 5) PDF
     Board from session 1 PDF
     Board from session 2 PDF
     Board from session 3 PDF
     Board from session 4 PDF
     Board from session 5 PDF
     Board from session 6 PDF
     Board from session 7 PDF
     Board from session 8 PDF
     Board from session 9 PDF
     Board from session 10 PDF

Exercises (in French, with solutions)

     Exercises Chapter 1 (SVD) PDF
     Sources for lab session 1 (PCA) [ZIP]
     Exercises Chapter 2 (PCA) PDF
     Exercises Chapter 3 (Linear models) PDF
     Exercises Chapter 4 (Linear regression) PDF
     Sources for lab session 2 (Linear regression) [ZIP]

Materials on this page are available under Creative Commons CC BY-NC 4.0 license.
La version française de cette page se trouve ici.