Statistique exploratoire multi modèle
Objectives
- With the explosion of big data problems statistical learning has become a very hot field. In this course, many linear and non-linear statistical methods are discussed and practiced. Teaching is resolutely focused on practice with R or Python practical works for each method (20% theory, 80% practice).
- Students will be able to optimize each model to compare them and ultimately select the most efficient method on the available data.
Description
- Lesson + practical work for each part :
Introduction : statistical learning, regression & classification – Linear models - GAM – Decision trees – Model aggregation methods (Bagging, Random forests, Boosting) – Support Vector Machines – Neural Networks & Deep Learning
Bibliography
- An introduction to statistical learning, G.James & al., Springer
- The elements of statistical learning, T.Hastie & al., Springer
- https://cran.r-project.org/
Pre-requisites
- R & Python, inferential statistics, gaussian linear model, logistic regression