

MTH 5810  Mathematical Methods for Machine Learning4 lecture hours 0 lab hours 4 creditsCourse Description This course surveys the essential linear algebra and multivariate calculus required for graduate study in machine learning. Topics include matrix algebra, real vector spaces, inner product spaces, differentiation and Newton's method, partial differentiation, the gradient, the chain rule, and optimization of multivariate functions. Prereq: Enrollment in machine learning graduate program Note: This course is not open to undergraduate students. Course Learning Outcomes Upon successful completion of this course, the student will be able to:
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Course Topics
Coordinator Dr. Anthony van Groningen 

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