EE-566: Adaptation and learningIn this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.
ME-371: Discretization methods in fluidsCe cours présente une introduction aux méthodes d'approximation utilisées pour la simulation numérique en mécanique des fluides.
Les concepts fondamentaux sont présentés dans le cadre de la méthode d
PHYS-641: Quantum ComputingAfter introducing the foundations of classical and quantum information theory, and quantum measurement, the course will address the theory and practice of digital quantum computing, covering fundament
EE-726: Sparse stochastic processesWe cover the theory and applications of sparse stochastic processes (SSP). SSP are solutions of differential equations driven by non-Gaussian innovations. They admit a parsimonious representation in a
CS-602: Foundation of probabilistic proofsProbabilistic proof system (eg PCPs and IPs) have had a tremendous impact on the theoretical computer science, and have also found practical uses. They underlie delegation of computation protocols and
