MATH-520 / 5 crédits

Enseignant: Chizat Lénaïc

Langue: Anglais


Summary

Mathematical analysis of modern supervised machine learning techniques, from linear methods to artificial neural networks.

Content

Keywords

Supervised learning, Machine learning, Neural networks, Optimization, Statistics

Learning Prerequisites

Required courses

Analysis, Linear Algebra, Probability and Statistics

Important concepts to start the course

  • A good knowledge of undergraduate mathematics is important.

  • Ability to code in a scientific computing programming language of your choice (e.g. Python, Matlab, Julia). The course will involve coding exercises and assignments.

  • Having followed an introductory class on machine learning is beneficial.

 

Learning Outcomes

By the end of the course, the student must be able to:

  • Contextualise the research literature on theoretical machine learning
  • Interpret he practical behavior of complex machine learning pipelines through the lens of mathematical theory
  • Implement simple supervised learning algorithms from scratch
  • Reason on how statistical and optimization phenomena interact in machine learning practice
  • Distinguish between what is known and what is not known in the theory of deep learning

Assessment methods

Homeworks, projects, presentation

Resources

Moodle Link

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Topics in machine learning
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Topics in machine learning
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Topics in machine learning
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Topics in machine learning
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22