Phelma Formation 2022

5PMBMLD0 : Introduction to Machine learning and Deep learning - WPMBXAS2

  • Number of hours

    • Lectures 12.0
    • Projects 0
    • Tutorials 0
    • Internship 0
    • Laboratory works 12.0


    ECTS 3.0


Introduction to the statistical learning theory and prediction (regression/classification)

  • Review of Models/Algorithms for supervised/unsupervised learning
  • Illustration de ces algorithmes sur différents jeux de données on different dataset
    (intelligence artificielle, Bioinformatics, vision, etc ...)
Contact Alice CAPLIER


  • General introduction to the statistical learning theory and prediction (regression/classification)
  • Generative approaches: Gaussian discriminant analysis, naïve Bayes hypothesis
  • Discriminative approaches: logistic regression
  • Prototype approaches: support vector machines (SVM)
  • Unsupervised classification (kmeans and mixture model)
  • Dictionnary learning / Sparse reconstruction
  • Source separation


Basic elements of probability/statistics, filtering


Semester 9 - The exam is given in english only 

Rapport de BE : 50%
Examen Ecrit : 50%

Additional Information

Semester 9 - This course is given in english only EN

Course list
Curriculum->Double-Diploma Engineer/Master->Semester 9
Curriculum->Master->Semester 9


  • Trevor Hastie, Robert Tibshirani et Jerome Friedman (2009), "The Elements of Statistical Learning," (2nd Edition) Springer Series in Statistics
  • Christopher M. Bishop (2006), "Pattern Recognition and Machine Learning," Springer
  • Richard O. Duda, Peter E. Hart et David G. Stork (2001), "Pattern classification," (2nd edition) Wiley