This course has three objectives:
The first one is to introduce mathematical statistics tools and statistical learning ("machine learning"). We will describe everything from the choice of a statistical model, to parameter estimation, inference and model selection. We will learn how to build estimators, tests and classification rules, and how to evaluate the performance of these rules. We will introduce a number of theoretical tools - decision theory, empirical process.
The second objective is to describe, in the course and in small classes, concrete examples of modeling in various fields (signal and image processing, econometrics, environmental sciences, shape classification, etc.).
The third objective is to develop a well-founded practical savoir-faire enabling students to understand how theoretical tools can be implemented in concrete applications (use of R or Python).
The last two courses will be devoted to an introduction to statistical learning.
Evaluation: Written exam, two take-home assignments, two quizzes.
- Teaching coordinator: Chennetier Guillaume
- Teaching coordinator: Dieuleveut Aymeric
- Teaching coordinator: Forghieri Orso
- Teaching coordinator: Fort Gersende
- Teaching coordinator: Gabrié Marylou
- Teaching coordinator: Gadat Sébastien
- Teaching coordinator: Gaucher Renaud
- Teaching coordinator: Lerasle Matthieu
- Teaching coordinator: Moulines Eric
- Teaching coordinator: Rakotomalala Matthias