3180. Mathematics for Machine Learning

Applications of elementary linear algebra, probability theory, and multivariate calculus to fundamental algorithms in machine learning. Topics include the theory of orthogonal projection, bilinear forms, and the spectral theorem to multivariate regression and principal component analysis; optimization algorithms such as gradient descent and Newton's method applied to logistic regression; and convex geometry applied to support vector machines. Other topics include Bayesian probability theory and the theory of convolution especially as applied to neural networks. Theory illustrated with computer laboratory exercises.

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Last Refreshed: 02-MAY-24 05.20.18.463427 AM

Enrollment Data	Term	Class Number	Campus	Instruction Mode	Instructor	Section	Schedule	Location	Enrollment
1243 6395 1 001	Spring 2024	6395	Storrs	In Person	Lee, Kyu-Hwan	001	TuTh 9:30am‑10:45am	MONT 319	30/30