COM-417: Advanced probability and applicationsIn this course, various aspects of probability theory are considered. The first part is devoted to the main theorems in the field (law of large numbers, central limit theorem, concentration inequaliti
MATH-485: Introduction to stochastic PDEsStochastic PDEs are used to model systems that are spatially extended and include a random component. This course gives an introduction to this topic, including some general measure theory, some Gauss
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
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
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-432: Probability theoryThe course is based on Durrett's text book
Probability: Theory and Examples.
It takes the measure theory approach to probability theory, wherein expectations are simply abstract integrals.