Machine Learning

Information

Teacher coordinatorClément Chatelain
Teacher(s)Clément Chatelain, Benoît Gaüzère, Simon Bernard
LanguageEnglish
Credits4.5
TeachingLectures : 18h Exercises : 18h
OptionData science
Web sitehttps://moodle.insa-rouen.fr/course/view.php?id=200

Aim and objective

The purpose of this lecture is to familiarize the student with learning and data mining methods on huge amount of data.

Outcome learning

  • INSA reference data :
    • Concevoir un système d'ingénierie des données [3P]
    • Déterminer des classes de problèmes [3P]
  • CNISF reference data :
    • J40K [1P]
    • J10C [2I]
1 - Notion, 2 - Concept, 3 - Application, I - fully, P - incomplete

Course description

  • Non linear methods for data mining
  • Kernel machine (SVM)
  • Deep learning (CNN, LSTM, etc.)
  • Decision trees and random forests

Prerequisites

  • Introduction to machine learning

Bibliography

  • Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, springer, 2001
  • John Shawe-Taylor and Nello Cristianini Kernel Methods for Pattern Analysis, Cambridge University Press, 2004
  • Bernhard Schölkopf and Alex Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.

Assessment

  • Project: 40%
  • Final practical exam: 60%