The objective of this course is to provide the principles of advanced (supervised and unsupervised) machine learning algorithms, and explain their interest when applying them to address learning tasks using visual, auditory or textual data, as well as multi-modal combinations.

In a very broad acceptation, shape and topology optimization is about finding the best domain (which may represent, depending on applications, a mechanical structure, a fluid channel,…) with respect to a given performance criterion (e.g. robustness, weight, etc.), under some constraints (e.g. of a geometric nature).

This interdisciplinary course is designed for students with a computer science or mathematical background, offering them an opening to bioinformatics and to bioinformatics and computational biology.

This course consists on a cycle of seminars given by different industrials and on a projet related to the manipulation of real data.

This course presents a range of mathematical tools and methods widely used in a large variety of fields: signal and image processing, numerical schemes for partial differential equations, scientific visualization, deep neural network… This teaching is structured and delivered in a complementary way: the first part provides the theoretical frameworks, the second part focuses on image processing methods using wavelet transforms and/or partial equations, and the last part is devoted to other applications in recent research areas involving wavelets.

Theoretical foundations of convex optimization.

Equations and models are presented in a continuum setting, and then approximated in time and space. Then, the efficient numerical resolution is addressed with some examples of practical applications.

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

In the current context of energy transition and fight against global warming, a precise knowledge of the crust, down to several km depth, has become a critical issue.

In this course, we will introduce parallel programming paradigms to the students in the context of applied mathematics.