Mathematical Foundations of Data Science

Modern data science methods and the mathematical foundations: linear regression, classification and clustering, kernel methods, regression trees and ensemble methods, dimension reduction.

High-Dimensional Statistics

The goal of this PhD level graduate course is to provide a rigorous introduction to concepts and methods of high-dimensional statistics 

having numerous applications in machine learning, data science and signal processing.

College Algebra

Study of the properties of algebraic, exponential, and logarithmic functions as needed for pre-calculus and calculus.

Bridge to Mathematics

Special Topics course "Bridge to Mathematics" by Anton Leykin, for the Honors Program section and a general section.

Probability Theory

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. 


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