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).

Gradient-based global sensitivity analysis – Master M2RI 2023

Principal component analysis and applications

Bootstrap, Bagging, Random forests

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

GARCH model: Application to volatility forecasting (slides in French)

Stochastic processes, martingales, brownian motion and stochastic calculus (in French)