Coursebooks

Statistical machine learning

MATH-412

Lecturer(s) :

Thibaud Emeric Rolland Georges

Language:

English

Summary

A course on statistical methods for supervised and unsupervised learning.

Content

Learning Prerequisites

Required courses

Analysis, Linear Algebra, Probability and Statistics, Linear Models

Important concepts to start the course

This is a statistics/mathematics course. Prior to following this course, the student must have very good knowledge of basic probabilty and statistics (statistical modeling and inference, linear regression).

Learning Outcomes

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

Teaching methods

Ex cathedra lectures, exercises and computer practicals in the classroom and at home.

Assessment methods

Data analysis (mini-)project, and final exam.

Seconde tentative : Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.

Supervision

Office hours No
Assistants Yes
Forum Yes

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

Ressources en bibliothèque
Notes/Handbook

A polycopié will be available on Moodle.

In the programs

Reference week

 MoTuWeThFr
8-9   BC03 
9-10    
10-11    CM012
11-12    
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German