Machine Learning


Teacher coordinatorClément Chatelain
Teacher(s)Clément Chatelain, Benoît Gaüzère, Simon Bernard
TeachingLectures : 18h Exercises : 18h
OptionData science
Web site

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


  • Introduction to machine learning


  • 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.


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