EE-559: Deep learningThis course explores how to design reliable discriminative and generative neural networks, the ethics of data acquisition and model deployment, as well as modern multi-modal models.
MGT-499: Statistics and data scienceThis class provides a hands-on introduction to statistics and data science, with a focus on causal inference, applications to sustainability issues using Python, and dissemination of scientific result
ENG-466: Distributed intelligent systemsThe goal of this course is to provide methods and tools for modeling distributed intelligent systems as well as designing and optimizing coordination strategies. The course is a well-balanced mixture
MATH-230: ProbabilityLe cours est une introduction à la théorie des probabilités. Le but sera d'introduire le formalisme moderne (basé sur la notion de mesure), de lier celui-ci à l'aspect "intuitif" des probabilités mais
MATH-496: Computational linear algebraThis is an introductory course to the concentration of measure phenomenon - random functions that depend on many random variables tend to be often close to constant functions.
FIN-415: Probability and stochastic calculusThis course gives an introduction to probability theory and stochastic calculus in discrete and continuous time. The fundamental notions and techniques introduced in this course have many applicatio
MATH-330: Martingales and Brownian motionIntroduction to the theory of discrete-time martingales including, in particular, the convergence and stopping time theorems. Application to branching processes. Introduction to Brownian motion and st
