CS-456: Deep reinforcement learningThis 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 approac
CS-450: Algorithms IIA 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 reper
MGT-581: Introduction to econometricsThe course provides an introduction to econometrics for economics and financial applications. The objective is to learn how to make valid (i.e., causal) inference from economic and social data.
MATH-336: Randomization and causationThis course covers formal frameworks for causal inference. We focus on experimental designs, definitions of causal models, interpretation of causal parameters and estimation of causal effects.
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
MSE-421: Statistical mechanicsThis course presents an introduction to statistical mechanics geared towards materials scientists. The concepts of macroscopic thermodynamics will be related to a microscopic picture and a statistical
ENG-267: Estimation methodsLes étudiants traitent des observations entachées d'incertitude de manière rigoureuse. Ils maîtrisent les principales méthodes de compensation des mesures et d'estimation des paramètres. Ils appliquen
MGT-418: Convex optimizationThis course introduces the theory and application of modern convex optimization from an engineering perspective.
MGT-416: Causal inferenceStudents will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and
application will be balanced, with students working directly with network data th
