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Our engineering & Master degrees

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Machine statistical Learning - 5PMSAST6

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  • Number of hours

    • Lectures : 12.0
    • Laboratory works : 8.0
    ECTS : 2.0

Goals

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

  • 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

Tests

Semester 9 - The exam is given in english only 



Additional Information

Semester 9 - This course is given in english only EN

Curriculum->BIOMED->Semester 9
Curriculum->Master TSI SIGMA->Semester 9
Curriculum->Double-Diploma Engineer/Master->Semester 9
Curriculum->Master->Semester 9
Curriculum->SICOM->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
  • Richard O. Duda, Peter E. Hart et David G. Stork (2001), "Pattern classification," (2nd edition) Wiley

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Date of update January 9, 2017

Grenoble INP Institut d'ingénierie Univ. Grenoble Alpes