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UE Introduction to Machine Learning and Deep Learning

Degrees incorporating this pedagocial element :

Description

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 ...)

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

This course is given at Phelma-INP.

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