MAT 6400 Mathematics for Machine Learning
This Mathematics for Machine Learning course covers the essential mathematical foundations for understanding and developing machine learning algorithms. Topics include linear algebra, for representing data structures; analytic geometry, to interpret relationships between data points; matrix decomposition for efficient data processing; vector calculus for optimizing models; and probability theory to quantify uncertainties. These concepts are applied to core machine learning problems, including regression, dimensionality reduction, density estimation, and classification. Emphasis will be placed on utilizing technology/software (specifically Python) to do many of the computational aspects of the aforementioned topics. By building these skills, students will gain insights into model selection, data interpretation, and algorithm design, preparing them for advanced machine learning coursework and research.