*Course objective*: Introducing high-dimensional regression
and classification methods for biological data analysis.

*Organization*: The course is divided into five sessions. Each
session is made of a lecture and practical classes. In those in-class
practical classes, students are given the opportunity to tackle
problem-solving exercises using R.

The following documents are provided for each session:

- a video lecture: R tutorial whose slides, data and R script are
provided;

- a Rmd file containing in-class exercises.

Exam in 2021

Exam in 2020

## Session 1

*Objective*: Setting a statistical framework for linear
prediction of a real-valued or a k-class response variable.

## Session 2

*Objective*: Selecting the best subset of predicting variables
in linear regression and classification models.

## Session 3

*Objective*: Using penalized estimation procedures to estimate
high-dimensional regression and classification models.

## Session 4

*Objective*: Using latent variable models for estimating
prediction models with high-dimensional data.

## Session 5

*Objective*: Introducing nonlinearity in regression and
classification models using generalized additive modeling.