Diplômes intégrant cet élément pédagogique :
Descriptif
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
Pré-requis recommandés
Basic elements of probability/statistics, filtering