Student-led special topic courses which vary by semester.

An introduction to the fundamental scientific principles governing information science and engineering. Topics include: definition of information; entropy; information representation in analog and digital forms; information transmission; spectrum and bandwidth; information transformation including data compression, filtering, encryption, and error correction; information storage and display; and large-scale information systems. Technologies for implementing information functions.

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.

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)

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.

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

A second-level undergraduate course covering a topic not normally covered in the course offerings. The topic usually reflects new developments in the electrical and computer engineering field. Offering is based on student and faculty interests.

A second-level undergraduate course covering a topic not normally covered in the course offerings. The topic usually reflects new developments in the electrical and computer engineering field. Offering is based on student and faculty interests.

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)

Studies the modeling, analysis, design, computer simulation, and measurement of electrical circuits which contain non-linear devices such as junction diodes, bipolar junction transistors, 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 the studio mode with mixed lecture and lab. Prerequisite: ECE 2630, APMA 2130 co-requisite