• ECE 2200

    Applied Physics
     Rating

    4.71

     Difficulty

    1.47

     GPA

    3.91

    Last Taught

    Spring 2026

    An applied physics course in electricity and magnetism, with emphasis on the technologies derived from them. An integrated lab component will provide team-based, hands-on examples and reviews of key concepts. Calculus 3 (Multivariable) may be taken concurrently; however, students should be proficient with vectors and calculus, including the chain rule and trigonometric functions. Co-requisite:  APMA 2120 or equivalent, and Prerequisite: PHYS 1425 and APMA 1110 or equivalent.

  • ECE 2300

    Applied Circuits
     Rating

    4.00

     Difficulty

    4.00

     GPA

    3.59

    Last Taught

    Spring 2026

    This course introduces electrical engineering theory and its application to circuits containing active and passive circuit elements. Content includes fundamental concepts such as voltage, current, power, energy and Ohm's Law as well as circuit analysis techniques including node-voltage and mesh-current based on circuit laws and theorems such as Kirchhoff Laws, source superposition, and equivalent circuits. Prerequisite: Must have completed (APMA 1110 or MATH 1320) AND (ENGR 1624 or ENGR 1410 or ENGR 2595 Topic Engineering Foundations I or ENGR 1010)

  • ECE 2330

    Digital Logic Design
     Rating

    4.13

     Difficulty

    2.75

     GPA

    3.36

    Last Taught

    Spring 2026

    Introduction to analysis and design of digital systems from switches to gates to components to CPU. Analysis and design of combinational and sequential components including multiplexers and demultiplexers, decoders and encoders, comparators, adders and ALU, registers and register files, counters and timers, RTL design, culminating in the design of a simple programmable processor. 10-12 studio design activities. Cross-listed as CS 2330.

  • ECE 2410

    Intro to Machine Learning
     Rating

     Difficulty

     GPA

    3.67

    Last Taught

    Spring 2026

    Learn about and experiment with machine learning algorithms using Python. Applications include image classification, removing noise from images, and linear regression. Students will collect and interpret data, learn machine learning theory, build systems-level thinking skills required to strategize how to break the problem down into various functions, and to implement, test and document those functions. Prerequisite: CS 111X

  • ECE 2600

    Electronics
     Rating

    4.50

     Difficulty

    3.00

     GPA

    3.63

    Last Taught

    Spring 2026

    Studies the modeling, analysis, design, computer simulation, and measurement of electrical circuits which contain non-linear devices such as junction diodes and field effect transistors. Includes the gain and frequency response of linear amplifiers, power supplies, and other practical electronic circuits. This course is taught in a studio style with mixed lecture and lab. Pre or Corequisite: APMA 2130 and ECE 2700 AND Prerequisite: (ECE 2300 or ECE 2501 Topic Applied Circuits (link 15599)

  • ECE 2700

    Signals and Systems
     Rating

     Difficulty

     GPA

    3.31

    Last Taught

    Spring 2026

    Develops tools for analyzing signals and systems in continuous and discrete-time, for controls, communications, signal processing and machine learning. Primary concepts are the representation of signals and linear systems in the time domain (convolution, differential equations, state-space representation) and in the frequency domain (Fourier/Laplace analysis) including practical programming examples. Co-requisite: APMA 2130 or MATH 3250, and Prerequisite: (ECE 2300 or ECE 2501 Topic: Applied Circuits)

  • ECE 3103

    Solid State Devices
     Rating

    4.06

     Difficulty

    2.50

     GPA

    3.45

    Last Taught

    Spring 2026

    Analyzes the basics of band theory and atomic structure; charge-transport in solids; current voltage characteristics of semiconductor devices, including p-n junction diodes, bipolar transistors, Schottky diodes, and insulated-gate field-effect transistors; electron emission; and superconductive devices. Prerequisite: ECE 2300.

  • ECE 3250

    Electromagnetic Energy Conversion
     Rating

    3.17

     Difficulty

    3.25

     GPA

    3.39

    Last Taught

    Spring 2026

    Analyzes the principles of electromechanical energy conversion; three-phase circuit analysis; magnetic circuits and nonlinearity; transformers; electromagnetic sensing devices; DC, synchronous, stepper, and induction machines; equivalent circuit models; power electronic control of machines, switching regulators, Class D amplification. Laboratory, computer, and design exercises complement coverage of fundamental principles. Prerequisite: ECE 2300 and PHYS 2415 or ECE 2200

  • ECE 3251

    Electromagnetic Energy Conversion Lab
     Rating

     Difficulty

     GPA

    3.87

    Last Taught

    Spring 2026

    This lab provides practical exposure and continuation of the topics covered in the lecture sections of ECE 3250. Topics include principles of measurement and analysis using computerized instrumentation. Co-requisite ECE 3250

  • ECE 3430

    Introduction to Embedded Computer Systems
     Rating

    4.28

     Difficulty

    3.00

     GPA

    3.82

    Last Taught

    Spring 2026

    An embedded computer is designed to efficiently interact directly with its physical environment. This lab-based course explores architecture and interface issues relating to the design, evaluation and implementation of embedded systems . Topics include hardware and software organization, power management, digital and analog I/O devices, memory systems, timing and interrupts. Prerequisites: (ECE 2300 or ECE 2630) AND ECE 2330 AND CS 2130 all with a grade of a C- or better.