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Modern data science methods and the mathematical foundations: linear regression, classification and clustering, kernel methods, regression trees and ensemble methods, dimension reduction.
Independent research conducted under the guidance of a faculty member.
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.
Study of the properties of algebraic, exponential, and logarithmic functions as needed for pre-calculus and calculus.
Special Topics course "Bridge to Mathematics" by Anton Leykin, for the Honors Program section and a general section.
