Data Science

Information access and retrieval

This course addresses advanced aspects of information access and retrieval, focusing on several points: ...

Introduction to extreme-value analysis

Taking into account extreme events (heavy rainfalls, floods, etc.) is often crucial in the statistical approach to risk modeling.

Kernel methods for machine learning

Statistical learning is about the construction and study of systems that can automatically learn from data.

Level set methods and optimization algorithms with applications in imaging

This lecture will link levelĀ­set modeling of biomechanical systems (e.g. immersed elastic membranes mechanics) with optimal transportation theory.

Machine learning fundamentals

Understanding of fundamental notions in Machine Learning (inference, ERM and SRM principles, generalization bounds, classical learning models, unsupervised learning, semi-supervised learning.

Model exploration for approximation of complex, high-dimensional problems

Many industrial applications involve expensive computational codes which can take weeks or months to run. It is typical for weather prediction, in aerospace sector or in the civil engineering field.

Model selection for large-scale learning

When estimating parameters in a statistical model, sharp calibration is important to get optimal performances.

Modeling Seminar

This lecture proposes modelling problems. The problems can be industrial or academic.

Numerical optimal transport and geometry

Optimal transport is an important field of mathematics that was originally introduced in the 1700's by the French mathematician and engineer Gaspard Monge to solve the following very applied problem ...

Software Development Tools and Methods

This lecture presents various useful applications, libraries and methods for software engineering related to applied mathematics.