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:

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.