Phelma Formation 2022

5PMSAST6 : Machine Learning - WPMTDML0

  • Number of hours

    • Lectures 12.0
    • Projects 0
    • Tutorials 0
    • Internship 0
    • Laboratory works 8.0

    ECTS

    ECTS 2.0

Goal(s)

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

  • Review of Models/Algorithms for supervised/unsupervised learning
  • Illustration of these algorithms on different dataset
    (Artificial Intelligence, Bioinformatics, vision, etc ...)
Contact Florent CHATELAIN

Content(s)

  • 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


Prerequisites

Basic elements of probability/statistics, filtering

Test



Rapport de BE : 25%
Examen Ecrit : 75%

Additional Information

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

Bibliography

  • 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
  • Kevin P. Murphy (2012), "Machine Learning: a Probabilistic Perspective", The MIT Press