CS-233(a) / 4 crédits

Enseignant: Salzmann Mathieu

Langue: Anglais


Summary

Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.

Content

Keywords

Machine learning, classification, regression, algorithms

Learning Prerequisites

Required courses

Linear algebra

Important concepts to start the course

  • Basic linear algebra (matrix/vector multiplications, systems of linear equations, SVD).
  • Multivariate calculus (derivative w.r.t. vector and matrix variables).
  • Basic programming skills (labs will use Python).

Learning Outcomes

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

  • Define the following basic machine learning problems: regression, classification, clustering, dimensionality reduction
  • Explain the main differences between them
  • Derive the formulation of these machine learning models
  • Assess / Evaluate the main trade-offs such as overfitting, and computational cost vs accuracy
  • Implement machine learning methods on real-world problems, and rigorously evaluate their performance using cross-validation.

Transversal skills

  • Assess one's own level of skill acquisition, and plan their on-going learning goals.
  • Continue to work through difficulties or initial failure to find optimal solutions.

Teaching methods

  • Lectures
  • Lab sessions

Expected student activities

  • Attend lectures
  • Attend lab sessions
  • Work on the weekly theory and coding exercises

Assessment methods

  • Two graded exercise sessions (10% each).
  • Final exam (80%)

Supervision

Office hours No
Assistants Yes
Forum Yes
Others Course website

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Introduction to machine learning (BA3)
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Introduction to machine learning (BA3)
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Introduction to machine learning (BA3)
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Introduction to machine learning (BA3)
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9 CE4  INM202
INJ218
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     

Mardi, 8h - 10h: Cours CE4

Vendredi, 8h - 10h: Exercice, TP INM202
INJ218