MathALEA (Centrale A1)

Bayesian Statistics and Hierarchical Models (MathAppli)

Advanced Statistical Learning (MathAppli)

Statistics 2 (MathAppli)

Data Analysis (Info)

Statistics (BioSTIC)

Data Science with R (MathAppli)

scholarMathALEA (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:

as well as the Labs: and the data set related to the short questionnaire.

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

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

scholarBayesian 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:

You can get the lecture slides (annotated slides)

Here are some piece of code written during the lectures:

The Orthodontist dataset.

Instructions on how to grade are here!

scholarAdvanced 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)
  2. Model based clustering: lecture notes (annotated slides)

scholarStatistics 2 (MathAppli)

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

scholarData 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)

scholarStatistics (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:

Here are some pieces of code written during the lectures:

schoolWinter-Summer schools

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