Phelma Formation 2022

Machine statistical Learning - 5PMSAST6

  • Number of hours

    • Lectures 2.0
    • Projects 0
    • Tutorials 2.0
    • Internship 0
    • Laboratory works 14.0

    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: linear models and penalization, stochastic gradient
  • Model-free approaches: support vector machines (SVM)
  • Model-free approaches: decision trees and random forests
  • Unsupervised classification (kmeans, Gaussian mixture model, hierarchical methods)


Prerequisites
  • Basics of linear algebra
  • Notions of probability (random vectors, Gaussian vectors, conditional distributions)
  • Data analysis course: principal component analysis and dimension reduction
  • Advanced notions of statistics (inference and statistical decision theory)
  • Optimization: constrained optimization (KKT conditions), steepest descent algorithm

Test

Semester 9 - The exam is given in english only 

First session

  • ER assessment : 2 hours supervised written
  • EN assessment : work assignments + Lab reports

If distant learning mandatory:

  • ER assessment : 2 hours homework
  • EN assessment : work assignments + Lab reports

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Second session

  • EN assessment: Retaking this assessment is not possible


33% EN + 67% ER

Additional Information

Semester 9 - This course is given in english only EN

Course list
Curriculum->Engineering degree->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