Master Level

Machine Learning

This course is composed of 12 modules (each during 1h30) with a lecture (about 1h) and a short lab in R or Python (about 30 min). For the labs, students are pleased to bring a laptop (one laptop for 2 students is enough) and to download the material in advance. The studied machine learning methods will be illustrated on environmental data (weather forecast, ozone level) and biological data (gene expression, ...).

LABS are proposed. Each LAB gathers examples of codes in R and python to illustrate the methods introduced during the lecture notes (see Course material to get the slides) and some exercices.
A list of projects with real world datasets is proposed for homework (see Projects).

Machine Learning: A Probabilistic Perspective by Kevin Murphy.
Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman.
An introduction to statistical learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.

GO TO Course material, LABS, Projects

Graphical models (2017)

Lecture notes
Anger Ising model and Restricted Bolzman Machine (R markdown)
Ozone Gaussian Graphical Model (R markdown)
Customer's satisfaction survey, data : mobi.Rdata
Sachs protein data (R markdown)
Datasets: student_mat.csv, student_por.csv, student_description.txt, student-read-data.R

Modèles linéaires généralisés (2017)

GLM Pharmacologie Clinique et Epidémiologie

Séries temporelles et modèles à chaine de Markov cachée (Master 2, 2011)

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Introduction à matab/octave (Master Ingénieurie Financière, 2018)

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Bachelor/Licence Level

Introduction à la science des données - L3 maths (2024)

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Introduction aux réseaux de neurones - L2 maths (2024)

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Introduction à python - L2 maths


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Introduction à la science des données (2024)