Fiches de cours 2017-2018

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Applied machine learning

MICRO-455

Enseignant(s) :

Billard Aude

Langue:

English

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

Because machine Learning can only be understood through practice, by using the algorithms, the course is accompanied with practicals during which students test a variety of machine learning algorithm with real world data . The courses uses matlab libraries for machine learning, as well as the MLDEMOS TOOLBOX that entails a large variety of Machine Learning algorithms.

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

Learning Outcomes

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

Transversal skills

Teaching methods

Ex-cathedra, exercises, computer-based practical sessions

Expected student activities

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

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.

Assessment methods

Final written exam (75% grade), in-class assessment (25% grade).

Resources

Ressources en bibliothèque
Notes/Handbook

Machine Learning Techniques, available at the Librairie Polytechnique. To be purchased before the class starts.

Prerequisite for

Advanced Machine Learning, spring semester

Dans les plans d'études

  • Génie électrique et électronique , 2017-2018, Master semestre 1
    • Semestre
      Automne
    • Forme de l'examen
      Ecrit
    • Crédits
      4
    • Matière examinée
      Applied machine learning
    • Cours
      4 Heure(s) hebdo x 14 semaines
  • Génie électrique et électronique , 2017-2018, Master semestre 3
    • Semestre
      Automne
    • Forme de l'examen
      Ecrit
    • Crédits
      4
    • Matière examinée
      Applied machine learning
    • Cours
      4 Heure(s) hebdo x 14 semaines
  • Microtechnique, 2017-2018, Master semestre 1
    • Semestre
      Automne
    • Forme de l'examen
      Ecrit
    • Crédits
      4
    • Matière examinée
      Applied machine learning
    • Cours
      4 Heure(s) hebdo x 14 semaines
    • Cours
      4 Heure(s) hebdo x 14 semaines
  • Microtechnique, 2017-2018, Master semestre 3
    • Semestre
      Automne
    • Forme de l'examen
      Ecrit
    • Crédits
      4
    • Matière examinée
      Applied machine learning
    • Cours
      4 Heure(s) hebdo x 14 semaines
    • Cours
      4 Heure(s) hebdo x 14 semaines
  • Mineur en Systems Engineering, 2017-2018, Semestre automne
    • Semestre
      Automne
    • Forme de l'examen
      Ecrit
    • Crédits
      4
    • Matière examinée
      Applied machine learning
    • Cours
      4 Heure(s) hebdo x 14 semaines

Semaine de référence

LuMaMeJeVe
8-9 CO4
CO6
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
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