Rémi Flamary

Professional website

Theory of statistical learning M1


  • Introduction to machine learning and pattern recognition [PDF]
    • Introduction
    • Unsupervised learning
    • Supervised learning
    • Implementation
  • Linear regression [PDF]
    • Least Squares
    • Ridge regression
    • Variable selection with the Lasso
  • Linear classification [PDF]
    • Linear Discriminant Analysis
    • Logistic regression
    • Rosenblatt's perceptron
    • Support Vector Machines


  • Linear regression [PDF]
  • Linear classification

Practical sessions

Practical sessions will require a working Python environnement with te libraries Numpy/Scipy and Matplotlib installed. You can get such an environnement for Windows/Linux/MacOSX on Anaconda.

Here is a list of nice references for the Pythoon :

Project and competition

The final project is a Kaggle InClass competition Data Science UCA 2019 with real data. In order to participate to the competition, a Kaggle account is required. The competition is private and you will need the URL received by email to participate.

At the end of the competition, you will present as a group the data and the machine learning methods used in the competition. Then you will have a short presentation to explain what you did specifically during the competition. A report using IEEE double column format will also be part of the final grade.