Data analysis.

In this course, we will look at different statistical learning techniques. More specifically, we will look at unsupervised learning with principal component analysis and partitioning methods, and supervised learning with regression and classification methods. These methods will be implemented during 2 practical sessions using the R programming language. Afterwards, mini-projects will be proposed.

Introduction to data analysis and statistical learning
Unsupervised learning:
* Principal Component Analysis (PCA)
* Clustering (kmeans and hierarchical ascending classification)
Supervised learning:
* Simple and multiple linear regression
* Classification (knn, linear and quadratic discriminant analysis, naive Bayesian, logistic regression, decision trees and random forests).
Implementation of methods and methodologies with R software.

Unsupervised learning: PCA, partitioning. Supervised learning: regression, classification.
Filtering, classification, estimators, learning. R language.