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Apr 24, 2024
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MATH 6377 - Mathematics of Machine Learning Credit Hours: 3
Lecture Contact Hours: 3 Lab Contact Hours: 0
Prerequisite: Linear Algebra, Real Analysis (MATH 4331-4332), Probability.
This course is an introduction to the theoretical foundations of machine learning and is focused on the underlying mathematical concepts needed to understand the methods used in modern data science, without neglecting relevant algorithmic and computational aspects of the subject. Examples of covered topics might include - Support Vector Machines, Reproducing Kernel Hilbert Spaces, the Vapnik-Chervonenkis theory, concentration inequalities, dimensionality reduction and spectral clustering. This class is designed for graduate students interested in mastering theoretical tools underlying machine learning and data science.
Repeatability: No
Additional Fee: Y
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