Course Overview

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 ...)

http://phelma.grenoble-inp.fr/en/studies/5pmsast6-machine-statistical-learning-wpmbdas9

Learning Achievement

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 ...)

Competence

* 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

Course prerequisites

Basic elements of probability/statistics, filtering

Grading Philosophy

Written exam

Course schedule

Course type

Lecture

Online Course Requirement

Instructor

Florent Chatelain

Other information

Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
- April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
- September 1st, 2020 for Spring Semester intake (February – June).

Site for Inquiry