Phelma Formation 2022

Machine Learning - 5PMSAAU4

  • Number of hours

    • Lectures 14.0
    • Tutorials 4.0

    ECTS

    ECTS 1.5

Goal(s)

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 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



Additional Information

Course list
Curriculum->SICOM->Semester 5

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
  • Richard O. Duda, Peter E. Hart et David G. Stork (2001), "Pattern classification," (2nd edition) Wiley