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: 20-DEC-24 05.20.17.275138 AM

Enrollment Data	Term	Class Number	Campus	Instruction Mode	Instructor	Section	Schedule	Location	Enrollment
1253 6956 1 001	Spring 2025	6956	Storrs	In Person	Lee, Kyu-Hwan	001	TuTh 9:30am‑10:45am	MONT 319	33/30
1253 12380 1 801	Spring 2025	12380	Stamford	In Person	Kellinsky-Gonzalez, Kevin	801	We 3:35pm‑6:05pm	DWTN 129	17/40