5PMBMLD0 : Introduction to Machine learning and Deep learning - WPMBDAS9
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Number of hours
- Lectures : 12.0
- Tutorials : 0
- Laboratory works : 12.0
- Projects : 0
- Internship : 0
ECTS : 3.0
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 ...)
Contact Alice CAPLIER
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
PrerequisitesBasic elements of probability/statistics, filtering
Tests Semester 9 - The exam is given in english only 
Rapport de BE : 50%
Examen Ecrit : 50%
Additional Information Semester 9 - This course is given in english only
Curriculum->Double-Diploma Engineer/Master->Semester 9
Curriculum->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
- Richard O. Duda, Peter E. Hart et David G. Stork (2001), "Pattern classification," (2nd edition) Wiley
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Date of update March 18, 2019
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