### 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).

References

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)

Lab/homework
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)

page
du cours
#### Introduction à matab/octave (Master Ingénieurie
Financière, 2018)

page
du cours
### Bachelor/Licence Level

#### Introduction à python (2018)

page du cours