Catalog Description: This course offers an introduction to signals, systems, optimization, controls, and machine learning, all grounded in linear algebraic techniques. After a brief review of linear algebra, students will delve into topics such as signal processing, linear systems, feedback control, optimization methods, and foundational machine learning algorithms. Emphasizing practical applications, the course prepares EECS majors for advanced study by connecting mathematical concepts to real-world engineering problems.

Units: 4

Also Offered As: EECS 16A

Prerequisites: MATH 54

Formats:
Fall: 3.0 hours of lecture, 1.0 hours of discussion, and 3.0 hours of laboratory per week
Summer: 6.0 hours of lecture, 2.0 hours of discussion, and 6.0 hours of laboratory per week

Grading Basis: Student Option

Final Exam Status: Yes

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