MGT-448 / 4 crédits

Enseignant:

Langue: Anglais

## Summary

This course aims to provide graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data mining.

## Keywords

Supervised and unsupervised learning, Model selection, Generative models.

## Required courses

A course in basic probability theory.

## Recommended courses

linear algebra and statistics.

## Important concepts to start the course

Students should be familiar with basic concepts of probability theory, calculus and linear algebra.

## Learning Outcomes

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

• Formalize Formulate supervised and unsupervised learning problems and apply it to data.
• Understand and apply generative models.
• Understand and train basic neural networks and apply them to data.

## Transversal skills

• Assess one's own level of skill acquisition, and plan their on-going learning goals.

## Teaching methods

Classical formal teaching interlaced with practical exercices.

## Expected student activities

Active participation in exercise sessions is essential.

## Assessment methods

30% Homework

20% Midterm project

50% Final project

## Supervision

 Office hours Yes Assistants Yes Forum No

## Dans les plans d'études

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

## Semaine de référence

 Lu Ma Me Je Ve 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