Fiches de cours

Statistical inference and machine learning

MGT-448

Enseignant(s) :

Kiyavash Negar

Langue:

English

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.

Content

List of topics:

Keywords

Supervised and unsupervised learning, Model selection, Generative models.

Learning Prerequisites

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:

Transversal skills

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

20% Final project

30% Final exam

Supervision

Office hours Yes
Assistants Yes
Forum No

Dans les plans d'études

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11  BS150  
11-12    
12-13     
13-14BS150    
14-15    
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
 
      Cours
      Exercice, TP
      Projet, autre

légende

  • Semestre d'automne
  • Session d'hiver
  • Semestre de printemps
  • Session d'été
  • Cours en français
  • Cours en anglais
  • Cours en allemand