Professional website

**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

- Linear regression [PDF] [ECoG_Finger.npz]
- Linear classification [PDF][digits.npz]

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 :

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.