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-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.
MATH-413: Statistics for data scienceStatistics lies at the foundation of data science, providing a unifying theoretical and methodological backbone for the diverse tasks enountered in this emerging field. This course rigorously develops
FIN-417: Quantitative risk managementThis course is an introduction to quantitative risk management that covers standard statistical methods, multivariate risk factor models, non-linear dependence structures (copula models), as well as p
MATH-414: Stochastic simulationThe student who follows this course will get acquainted with computational tools used to analyze systems with uncertainty arising in engineering, physics, chemistry, and economics. Focus will be on s
MICRO-330: SensorsPrincipes physiques et électronique utilisés dans les capteurs. Applications des capteurs.
ME-390: Foundations of artificial intelligenceThis course provides the students with 1) a set of theoretical concepts to understand the machine learning approach; and 2) a subset of the tools to use this approach for problems arising in mechanica