Number of hours
- Lectures 12.0
- Projects 0
- Tutorials 0
- Internship 0
- Laboratory works 8.0
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 ...)
Content(s)
- 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
Test
Rapport de BE : 25%
Examen Ecrit : 75%
Additional Information
Course list
Curriculum->Master->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