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.

Undergraduate Research

Independent research conducted under the guidance of a faculty member.

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.


Subscribe to RSS - fa23