This course teaches the fundamentals needed to predict the behavior of real-world electronic phenomena and applications via mathematical models and circuit analytical methods that simplify initially complex problems, rendering them solvable and understandable. Among the specific topics to be covered are time and frequency domain representation, complex arithmetic, phasor analysis of systems governed by differential equations, Kirchhoff’s laws, physical examples of electronic elements and devices, RLC circuits, op amp-based signal processors, feedback methods, and circuit models for domains beyond the electronic. Class contact time includes lectures, discussions, and a laboratory component designed to help solidify learned concepts.

This course teaches the fundamentals needed to predict the behavior of real-world electronic phenomena and applications via mathematical models and circuit analytical methods that simplify initially complex problems, rendering them solvable and understandable. Among the specific topics to be covered are time and frequency domain representation, complex arithmetic, phasor analysis of systems governed by differential equations, Kirchhoff’s laws, physical examples of electronic elements and devices, RLC circuits, op amp-based signal processors, feedback methods, and circuit models for domains beyond the electronic. Class contact time includes lectures, discussions, and a laboratory component designed to help solidify learned concepts.

This course teaches the fundamentals needed to predict the behavior of real-world electronic phenomena and applications via mathematical models and circuit analytical methods that simplify initially complex problems, rendering them solvable and understandable. Among the specific topics to be covered are time and frequency domain representation, complex arithmetic, phasor analysis of systems governed by differential equations, Kirchhoff’s laws, physical examples of electronic elements and devices, RLC circuits, op amp-based signal processors, feedback methods, and circuit models for domains beyond the electronic. Class contact time includes lectures, discussions, and a laboratory component designed to help solidify learned concepts.

This course teaches the fundamentals needed to predict the behavior of real-world electronic phenomena and applications via mathematical models and circuit analytical methods that simplify initially complex problems, rendering them solvable and understandable. Among the specific topics to be covered are time and frequency domain representation, complex arithmetic, phasor analysis of systems governed by differential equations, Kirchhoff’s laws, physical examples of electronic elements and devices, RLC circuits, op amp-based signal processors, feedback methods, and circuit models for domains beyond the electronic. Class contact time includes lectures, discussions, and a laboratory component designed to help solidify learned concepts.

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.

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.

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.

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.

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.

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.