MasterNanosciences, nanotechnologies

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 ...)

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

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