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-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.
MATH-522: Empirical processesFrom prototypical examples of estimators used by statisticians, to more complex nonparametric models, methods and theorems will be taught to study their (non)asymptotic behavior, when defined as mappi
MATH-403: Randomized matrix computationsThis course is concerned with randomized algorithms that have been developed during the last decade to solve large-scale linear algebra problems from, for example, scientific computing and statistica
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.
COM-406: Foundations of Data ScienceWe discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. In particular we discuss fundamental ideas an
