Algorithms and learning for protein science
Proteins underlie all biological functions, yet, understanding their mechanisms at the atomic scale remains a fundamental open problem. The difficulties are inherent to complex dynamics in very high dimensional spaces. Indeed, with circa 5000 atoms and xyz coordinates per atom,
Statistical learning with extreme values
With the ubiquity of sensors, Big Data are now increasingly available in a wide variety of domains of human activity (science, industry, health, environment, commerce, security, …) and rare/extreme phenomena are becoming observable in a significant manner. Before, such events
Turing Seminar – An introduction to AGI Safety
The rapid advancements in artificial intelligence are showing no signs of slowing down. From GPT-2 to GPT-5, we remain uncertain about where this race for performance is leading us, though many experts warn of potentially catastrophic risks. The highly publicized
Regulating AI
This short course aims to unveil the ethical and regulatory shadows accompanying the development and use of AI techniques. It seeks to stimulate critical thinking and provide the necessary tools, frameworks, and perspectives required to navigate these uncharted territories.
Intelligence Artificielle et Environnement
L’objectif de ce cours est de relier la technologie qui bouleverse toutes les sciences et tous les secteurs (l’intelligence artificielle) aux enjeux majeurs pour l’humanité que sont le changement climatique et l’impact de l’activité humaine sur l’environnement. Il abordera de
Robotics
A large part of the recent progress in robotics has sided with advances in machine learning, optimization and computer vision. The objective of this lecture is to introduce the general conceptual tools behind these advances and show how they have
Stopping times and online algorithms
The objectives are to understand and master how to sequentially take that may have a strong impact on the future (typically depletion of budget). This course will be at the junction of mathematics, theoretical computer science and economics. More precisely,
Geometric data analysis
Choosing the correct loss, distance and convolution for every problem Machine learning sits at the intersection between modelling, software engineering and statistics: data scientists write programs that must satisfy domain-specific constraints, scale up to real data and can be trained
Projet de recherche reproductible / Reproductible research project
This is an MVA course about writing a reproducible edition of a scientific work. It could be considered the practical follow up of the MVA course Fondamentaux de la recherche reproductible et du logiciel libre, although both courses are independent.
Fondamentaux de la recherche reproductible et du logiciel libre / Fundamentals of reproducible research and free software
This is a course on free software (FS) and reproducible research including how to write and publish reproducible research, the legal aspects of the code, article, and data, and eventually the good practices to write free software and perform reproducible