3.00 credits
Prerequisites: MATH 2110Q and 2210Q. Recommended preparation: MATH 3160.
Grading Basis: Graded 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.3180. Mathematics for Machine Learning
Last Refreshed: 06-DEC-24 05.20.19.636277 AM
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
34/30
1253
12380
1
801
Spring 2025
12380
Stamford
In Person
Kellinsky-Gonzalez, Kevin
801
We 3:35pm‑6:05pm
DWTN 129
16/40
Spring 2025 Courses
To view current class enrollment click the refresh icon next to the enrollment numbers.