Fiches de cours 2017-2018

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Machine learning programming

MICRO-401

Enseignant(s) :

Billard Aude

Langue:

English

Summary

This is a practice-based course, where students program algorithms in machine learning and evaluate the performance of the algorithm thoroughly using real-world dataset.

Content

This programming class complements courses on machine learning given in the school. It offers students the possibility to understand some machine learning algorithms in depth by programming them and testing them rigorously. Students will be working in team of two. They will be offered a choice of methods to program. Programming can be done in matlab or C/C++. Proper evaluation of machine learning will be stressed out. Students will learn about various methods to evaluate machine learning methods (crossvalidation, grid search, F-measure, ROC curve, etc) and will be asked to put these in practice.

Keywords

Programming in C/C++ or matlab. Machine Learning. Statistics.

Learning Prerequisites

Required courses

Students must have taken a machine learning course or follow one during the same semester. This programming class is meant to complement the Applied Machine Learning course, but can also complement other machine learning courses given at EPFL.

Recommended courses

Applied Machine Learning - MICRO-455

Pattern Classification and Machine Learning: CS-433

Data Analysis and Model Classification - EE-516

Important concepts to start the course

Basic notions in Machine Learning:

Supervised versus unsupervised learning

Classification, non-linear regression, clustering

Learning Outcomes

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

Transversal skills

Teaching methods

Computer-based practice session. Some short ex-cathedra lectures will be given at the beginning of the class.

Expected student activities

Attendance to all sessions is necessary to progress rapidly and benefit from assistants' support.

Assessment methods

The students will be evaluated on the report and code handed out at the end of the course.

Supervision

Office hours No
Assistants Yes
Forum Yes

Resources

Ressources en bibliothèque

Prerequisite for

Students must know how to program either in Matlab or C/C++.

Dans les plans d'études

  • Microtechnique, 2017-2018, Master semestre 1
    • Semestre
      Automne
    • Forme de l'examen
      Pendant le semestre
    • Crédits
      2
    • Matière examinée
      Machine learning programming
    • TP
      2 Heure(s) hebdo x 14 semaines
  • Microtechnique, 2017-2018, Master semestre 3
    • Semestre
      Automne
    • Forme de l'examen
      Pendant le semestre
    • Crédits
      2
    • Matière examinée
      Machine learning programming
    • TP
      2 Heure(s) hebdo x 14 semaines

Semaine de référence

LuMaMeJeVe
8-9 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