Latest


2025.10.16: Material from session 5.
2025.10.15: Exercices V2.
2025.10.10: Correction board session 3.
2025.10.09: Exercises (V1.1), material from session 4.
2025.10.02: Material from session 3.
2025.09.25: Material from session 2.
2025.09.19: Material from session 1.
2025.09.15: The course webpage for Fall 2025 is online.

Instructor

Clément Royer
clement.royer@lamsade.dauphine.fr

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Optimization for Machine Learning

M2 IASD, Université Paris Dauphine-PSL, 2025-2026


Aim of the course

     Study the main optimization techniques used in machine learning and data science, as well as their underlying principles.

Course material

     Exercises (V2.0, Oct. 15) PDF

Session 1: Basics of optimization

     Intro slides PDF
     Virtual board PDF
     Backup slides (with material not covered in class) PDF

Session 2: Differentiation

     Virtual board PDF
     Notebook (with solutions) ZIP

Session 3: Gradient methods

     Virtual board (corrected version Oct. 10) PDF
     Notebook (with solutions) ZIP

Session 4: Nonconvex and nonsmooth optimization

     Virtual board PDF
     Illustration notebook ZIP

Session 5: Proximal methods

     Virtual board PDF
     Illustration notebook 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.