EE263
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Matrix Methods: Singular Value Decomposition
Electrical EngineeringENGR - School of Engineering
Course Description
Least-squares approximations of over-determined equations, and least-norm solutions of underdetermined equations. Range and nullspace and their connection to left and right inverses. Symmetric matrices and quadratic forms. Positive definite matrices. Newton's method. Vector Gaussian distributions. Eigenvalues and eigenvectors of symmetric matrices. Matrix norm and the singular-value decomposition. Spectral graph embedding. Low rank approximations. Emphasis on applications from a broad range of disciplines including circuits, signal processing, machine learning, and control systems.
Cross Listed Courses
Grading Basis
ROP - Letter or Credit/No Credit
Min
3
Max
3
Course Repeatable for Degree Credit?
No
Course Component
Lecture
Enrollment Optional?
No
Enforced Requisites
Prerequisite
Linear algebra such as ENGR 108 or MATH 104.
Does this course satisfy the University Language Requirement?
No
Schedule
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Courses
EE263 is a prerequisite for:
Programs
EE263 is a completion requirement for:
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EE263 is a prerequisite for: