L'objectif de ce cours est d'introduire les étudiants à la pensée algorithmique, de les familiariser avec les fondamentaux de l'Informatique et de développer une première compétence en programmation (langage C++). ...
This course focuses on dynamic models of random phenomena, and in particular, the most popular classes of such models: Markov chains and Markov decision processes. We will also study applications in queuing theory, finance, project management, etc. ...
A first graduate course in algorithms, this course assumes minimal background, but moves rapidly. The objective is to learn the main techniques of algorithm analysis and design, while building a repertory of basic algorithmic solutions to problems in many ...
This course provides an introduction to stochastic optimal control and dynamic programming (DP), with a variety of engineering
applications. The course focuses on the DP principle of optimality, and its utility in deriving and approximating solutions to an ...
This seminar teaches the participants to use advanced Python concepts for writing easier to read, more flexible and faster code.
It teaches concepts in a hands-on and tangible fashion, providing example use cases that all applied mathematicians can relate ...
This course provides an overview and introduces modern methods for reinforcement learning (RL.) The course starts with the fundamentals of RL, such as Q-learning, and delves into commonly used approaches, like PPO and DQN. The course will introduce student ...
The objective is to enable students to design advanced digital solutions for the control and the coordination of distributed dynamic systems, such as production or distribution energy systems, as well as intelligent transportation systems. ...
This course discusses advanced methods extensively used for the processing, prediction, and classification of temporal (multi-dimensional and multi-channel) sequences. In this context, it also describes key links between signal processing, linear algebra, ...