Teaching

STSCI 6840: Mathematics of Statistical Learning

Semester: Fall

Offered: 2026

Learning theory is an important branch of modern statistics. This course gives an overview of various topics and proof techniques that include: concentration inequalities, Bayes rules, reject option, margin condition, local averaging methods, universal consistency, empirical risk minimization, convex surrogate losses, Rademacher complexity, VC theory, structural risk minimization, sparse methods, low-rank regression, topic models, latent factor models and interpolation methods.

Prerequisites MATH 6710 or equivalent. Graduate Students only.

STSCI 4090: Theory of Statistics

Semester: Spring

Offered: 2026

Introduction to classical theory of parametric statistical inference that builds on the material covered in BTRY 3080. Topics include: sampling distributions, principles of data reduction, likelihood, parameter estimation, hypothesis testing, interval estimation, and basic asymptotic theory.

Prerequisites BTRY 3080 or MATH 4710 or equivalent and BTRY 3010 or equivalent.

Page updated

Google Sites

Report abuse