Units: 4
Hours: Lecture, 3 hours; discussion, 1 hour
Catalog page 355
Prerequisites: graduate standing; or consent of instructor
Description: Covers fundamental concepts for advanced study in electrical engineering, robotics, machine learning, and data science. Includes vector spaces; partitioned, unitary, and positive definite matrices; differential calculus with matrices; matrix decompositions; non-diagonalizable matrices; solution of linear equation systems; gradient descent and Newton?s method; introduction to linear optimization; the Lagrangian method.
Credit: May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.