Statistical learning of high-dimensional biological data

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 2024

Exam in 2023

Introducing case study

• Data and a brief R script

Session 1

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

• Video lecture with slides, R script and data (1, 2 and sleep);
  
• In-class activities: exercise and data.

Session 2

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

• Video lecture with slides, R script and data (1 and 2);
  
• In-class activities: exercise and data.

Session 3

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

• Video lecture with slides, R script and data (1 and 2);
  
• In-class activities: exercise and data (1 and 2).

Session 4

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

• Video lecture with slides, R script and data (1 and 2);
  
• In-class activities: exercise and (1 and data 2).

Session 5

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

• Video lectures (1 and 2) with slides, R script and data;
  
• In-class activities: exercise and data.