In this section you will find some teaching material: textbook, slides, R code…
Feel free to use / adapt them for your own needs (simply cite the source).
Principal component analysis and applications
 Slides
 Applets (with Shiny)
Bootstrap, Bagging, Random forests
 Slides
 R codes
Introduction to statistical learning (slides, 3h)
Introduction to linear regression (slides in French, 9h)
Basics on linear regression with a small number of explanatory variables.
 course 1 : Introduction : definition, interpretation and relevancy
 course 2 : Estimation : least squares, maximum likelihood
 course 3 : Influence of one predictor : significance test, ANOVA table
 course 4 : Geometrical interpretation. Prediction performance and model validation
Introduction to time series (textbook in French)
 Outline :
 Descriptive statistics. Forecast by exponential smoothing
 Probabilistic framework. SARIMA models. Box and Jenkins methodology
 Forecast with a probabilistic model
 Link : website of the book “Forecasting: Methods and Applications” by Makridakis, WheelWright, Hyndman

Support : R tutorial for time series
GARCH model: Application to volatility forecasting (slides in French)
Stochastic processes, martingales, brownian motion and stochastic calculus (in French)
 Stochastic processes – Generality and examples
 Textbook on martingales
 List of exercices