This course addresses advanced aspects of information access and retrieval, focusing on several points: ...
Taking into account extreme events (heavy rainfalls, floods, etc.) is often crucial in the statistical approach to risk modeling.
Statistical learning is about the construction and study of systems that can automatically learn from data.
This lecture will link levelset modeling of biomechanical systems (e.g. immersed elastic membranes mechanics) with optimal transportation theory.
Understanding of fundamental notions in Machine Learning (inference, ERM and SRM principles, generalization bounds, classical learning models, unsupervised learning, semi-supervised learning.
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.
When estimating parameters in a statistical model, sharp calibration is important to get optimal performances.
This lecture proposes modelling problems. The problems can be industrial or academic.
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 ...
This lecture presents various useful applications, libraries and methods for software engineering related to applied mathematics.