MICRO-455 / 4 crédits

Enseignant: Billard Aude

Langue: Anglais


Summary

Real-world engineering applications must cope with a large dataset of dynamic variables, which cannot be well approximated by classical or deterministic models. This course gives an overview of methods from Machine Learning for the analysis of non-linear, highly noisy and multi dimensional data

Content

Keywords

Machine Learning, Statistics

Learning Prerequisites

Required courses

Linear Algebra, Probability & Statistics

Important concepts to start the course

Linear Algebra: Eigenvalue and singular value decomposition

Statistics: Definitions of probability density function, marginal, likelihood, covariance, correlation

Optimization: Lagrange multipliers, gradient descent, local and global optima

 

Teaching methods

The format of the course is that of a Split-Class: The theory of the course is presented through a video which students must watch prior to class. One hour of the course is allocated for this. This is following by an ex-cathedra lecture that consists of a one-hour interactive lecture session. The interactive lecture takes place on campus, but students who need it can also attend through zoom. An electronic polling system is used to engage students during the lecture.

A two-hour exercise sessions is given each week after the lecture. 4 of the weeks of the course are dedicated to computer-based practical sessions, during which students learn to use the algorithms seen in class for processing real data.

Exercise sessions and computer-based practice session are done on site only. No remote connection possible.

Expected student activities

Students are expected to watch the videos prior to the interactive lecture, as the interactive lecture will not repeat the video but go in more depth in the concepts presented in the videos.

Students are expected to attend the exercise sessions and the computer-based practice sessions. They should revise the class notes prior to going to practical session to be on top of the the theoretical material prior to applying it.

Students who are no longer up to date with the pre-requisites should work on these in parralel to taking the class.

Assessment methods

Final written exam (100% grade), in-class assessment through a quiz (0% grade).

Supervision

Office hours No
Assistants No
Forum Yes

Resources

Virtual desktop infrastructure (VDI)

No

Notes/Handbook

The course is accompanied with lecture notes entitled: Machine Learning Techniques, available as a pdf posted on the class's website.

Moodle Link

Videos

Prerequisite for

MICRO-570 Advanced Machine Learning, spring semester

MICRO-462 Learning and adaptative control for robots

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Applied machine learning
  • Cours: 4 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11   CE2
BC07-08
 
11-12    
12-13    
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Jeudi, 9h - 10h: Cours

Jeudi, 10h - 13h: Cours CE2
BC07-08