EE-411 / 4 crédits

Enseignant: Krzakala Florent Gérard

Langue: Anglais


Summary

This is an introductory course in the theory of statistics, inference, and machine learning, with an emphasis on theoretical understanding & practical exercises. The course will combine, and alternate, between mathematical theoretical foundations and practical computational aspects in python.

Content

Keywords

Statisitics, Supervised and unsupervised learning

Learning Prerequisites

Required courses

* Basic probability theory.

* Basic knowlegde of python programing

* Basic linear algebra, and calculus

 

 

 

 

 

 

Recommended courses

Probability and statistics.

Advance Python

Basic optimization

 

Important concepts to start the course

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

 

 

 

Learning Outcomes

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

  • Formulate statistical models and apply them to statistical learning
  • Apply machine learning technics to data science problems
  • Solve concrete data science problems
  • Explain and understand the fundamental principle of learning theory

Assessment methods

* Homeworks during the lectures

* Final project,

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Fundamentals of inference and learning
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Fundamentals of inference and learning
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 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