Probability Theory for Statistics and Data Science

3 units

This is a graduate-level course on the probability theory underpinning modern statistics and data science. The course will start with a review of key concepts from an undergraduate probability course and an introduction to basic measure-theoretic concepts. The course will then cover modes of stochastic convergence, classical results such as the law of large numbers and central limit theorem, concentration inequalities, and time-permitting, topics such as martingales and U-statistics. The course is intended for master’s and PhD students in statistics and other quantitative disciplines. Proofs will not be an emphasis in the course.