This course introduces the essential statistical theory for machine learning. The course focuses on the theory of statistical inference and statistical learning, providing a rigorous treatment of a range of estimation and prediction problems. These include, for example, linear regression, classification, matrix estimation, nonparametric statistics, and neural networks. The course is theoretical and is intended for students who are interested in mathematical and statistical foundation of machine learning.
The following table contains a list of all graduate special topics courses offered by the School of Math within the last 5 years. More information on courses offered in the current/upcoming semester follows below.
Modern data science methods and the mathematical foundations: linear regression, classification and clustering, kernel methods, regression trees and ensemble methods, dimension reduction.
This course is a mathematical introduction to probability theory, covering random variables, moments, multivariate distributions, law of large numbers, central limit theorem, and large deviations.
MATH 3215, MATH 3235, and MATH 3670 are mutually exclusive; students may not hold credit for more than one of these courses.
Introduction to probability, probability distributions, point estimation, confidence intervals, hypothesis testing, linear regression and analysis of variance.
MATH 3215, MATH 3235, and MATH 3670 are mutually exclusive; students may not hold credit for more than one of these courses.
This course will cover important topics in linear algebra not usually discussed in a first-semester course, featuring a mixture of theory and applications.
