MathALEA (Centrale A1)

This course is your first course in statistics where you will learn about descriptive analysis, principal component analysis, hypothesis testing, likelihood inference and linear models. You can get the lecture material (annotated slides for Groups:A-F and G-L).

Furhter here are the exercise sheets:

• Hypothesis testings
• Maximum likelihood estimator
• Linear models

as well as the Labs:

• Get familiarized with R (quarto)
• Hypothesis testing (quarto)
• Descriptive analysis (quarto)
• Linear models (quarto)
• Principal component analysis (quarto)

and the data set related to the short questionnaire.

I list here some code done during lecture (or related to what we did)

• Poisson model for the number of goals

Rien à voir mais les supports pour la présentation de l'option.

Bayesian Statistics and Hierarchical Models (MathAppli)

This course is an 'advanced' lecture on Bayesian statistics with an emphasis on computational statistics for Bayesian studies. During the lecture we will:

• briefly review the core concepts in Bayesian inference;
• introduce MCMC samplers (Metropolis--Hastings and Gibbs);
• talk about (Bayesian) hierarchical models;
• briefly talk about mixture models.
• intrudoce approximate Bayesian computation.
• apply methodologies to various applications.

You can get the lecture slides (annotated slides)

Here are some piece of code written during the lectures:

• Nuclear plants
• Bivariate Normal (Gibbs sampler)
• Football (1st simple Bayesian model)
• Bioassay
• Motorette
• Orthodontist (+kiwi)
• Coagulation and R cheat-sheet
• Galaxies

The Orthodontist dataset.

Instructions on how to grade are here!

Advanced statistical learning (MathAppli)

While Bertrand Michel taught various statistical learning techniques, I will focus on two main themes: reinforcement learning and model based clustering. The lecture materials are available below:

1. Reinforcement learning: lecture notes (annotated slides)
   • Lab 0: Introduction to the Gymnasium framework (Notebook)
   • Lab 1: The Self driving taxi (q-learning) (Notebook)
   • Lab 2: The cart pole challenge (REINFORCE) (Notebook)
2. Model based clustering: lecture notes (annotated slides)
   • Lab 3: Gaussian mixtures (Notebook)
   • Lab 4: Some applications of Gaussian mixtures (Notebook)

Statistics 2 (MathAppli)

This course is divided in 3 independent parts (they will appear in due time):

• Time series: lecture ( annotated slides), Exercise sheet
• Geostatistics: lecture (annotated slides), Lab.

Data Analysis (Info)

This course is divided into 5 chapters and aims at providing the basic element for any statistical analysis. Consequently we will talk about descriptive statistics, data visualization, inferential statistics, clustering, principal component analysis and linear models. As this lecture is given to informatic guys, most of the theoretical development will not be covered. Here is the lecture material (annotated slides)

Statistics (BioSTIC)

This lecture is divided into two parts. The first one is about statistical learning and will cover unsupervised classification using k-means, principal component analysis and logistic regression. The second part is about survival analysis and will talk about specificities of survival data, non parametric estimation of survival curves, comparison of survival curves and Cox's proportional hazard model. The lecture materials for this course are here (survival analysis (annotated), statistical learning (annotated)).

Labs sheet are listed below:

• TP1 k-means in practive with an application on Fisher's iris and italian wine datasets. (Rmd)
• TP2 Using PCA on a socio-economical (thanks André Mas!) and football players (Rmd)
• TP3 On the use of logistic regression on the survial on the Titanic and survival for cancer (Rmd)
• TP4 Survival analysis with R. (.Rmd)

Finally, here is some piece of code and dataset we used during lectures.

• Hold your breath dataset: data, code
• Test d'hypothèse:Notebook

Extreme value theory (Toulouse)

This course is an introduction to the univariate extreme value theory with a view towards financial and environmental applications. You can download the slides (annotated slides) as well as the Lab sheet. You can also get the course material for the Bayesian statistics we covered in class.

I list below some code used during the lecture

• First GEV modelling on Société Générale asset
• Modelling record temperature at Agen
• Modelling record temperature at Agen in a Bayesian framework
• Lethal dose modelling (quarto)

Data Analysis (Ecole Centrale Casablanca)

This course is divided into 2 independent chapters. The first one is about Geostatistics where you will learn basic concepts such as gaussian random fields, variogram fitting, kriging and (conditional) simulations. The second chapter is about machine learning where CART, random forest and boosting will be covered. All lecture materials (including Labs) are given below:

1. Lectures materials (annotated slides)
2. Labs:
   • Geostatistics (html, Rmd)
   • Random Forests (notebook)
   • Boosting (notebook)

Winter-Summer schools

Occasionaly I lecture for winter/summer schools. Here are the materials for the last one.

• Lecture slides
• Lecture 1 video
• Lecture 2 video