Easiest courses in Electrical and Computer Engineering
Looking for a gentler semester? Start with courses where students have earned higher grades.
Highest historical GPA first. At least 100 letter grades over the five years through the
selected term. Grades reflect past outcomes, not workload or a guaranteed result.
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47 courses
Ranked courses have at least 100 recorded letter grades.
Introduction to doctoral research in ECE covering energy, physics, fields, plasmas, comms, controls, photonics, devices, computing, plus writing and ethics skills.
Hands-on lab course introducing various sensor types and addressing implementation issues like interference, isolation, linearity, amplification, and grounding in electronic circuits.
Introductory Microprocessor Laboratory covers software and hardware experiments with microcomputers, including assembly language programming, I/O interfacing, and interrupt processing.
Optoelectronics Lab covers light detection, generation, and transmission with LEDs, lasers, and optical fibers; students perform optical characterization measurements.
The primary burden came from writing 5-8 page individual lab reports after each session. Students noted these reports required detailed sections and took approximately five hours to complete.
The workload involved reading papers and completing a semester-long project. While students found the paper readings challenging, they described the exams as straightforward or easy relative to the course content.
Laboratory course on terminal characteristics, speed control, voltage regulation, and system applications of electric machines, emphasizing experimental problem-solving.
Computer Vision covers fundamentals of image analysis, including acquisition, geometry, enhancement, scene characteristics, segmentation, shape and texture analysis, and parallel algorithms.
Digital Engineering Laboratory covers practical computer system design, including designing, constructing, testing, and programming pipelined digital computers and significant digital subsystems.
Embedded Microprocessor System Design covers hardware and software design for modern microprocessor-based embedded systems, focusing on the design process and a major team project.
Homework was reported as excessive, even when completed in groups of four, with students finding the coding assignments demanding despite the perceived lack of educational value.
Covers power converter principles, two-axis models, and dynamic behavior of AC drives. Includes simulation and analytical modeling of induction and synchronous motor systems.
A laboratory course covering Kirchhoff's laws, inductors, operational amplifiers, and frequency response through hands-on circuit assembly and measurement.
The labs were described as lengthy, and the final test was open book, open note, and open internet. However, students noted that the practice bench exam did not resemble the actual exam.
Covers probabilistic, algebraic, and geometric data models, mathematical analysis of learning algorithms and optimization methods, and ML applications.
Software Engineering covers designing and modifying large software systems, focusing on processes, architectures, design patterns, and testing, with a substantial team-based programming project.
Covers sampling, reconstruction, DFT spectral analysis, FFT, z-transforms, FIR/IIR filter design, signal flow graphs, and filter implementation in digital signal processing.
The course was described as extremely hard, particularly for undergraduates, with one student noting it made their brain hurt. TAs and the professor went the extra mile to break down difficult concepts.
Computers in Medicine covers microprocessor-based medical instrumentation, emphasizing real-time ECG analysis through labs and projects involving biomedical digital signal processing algorithms.
The course demanded substantial effort outside of class time, as classwork alone did not provide a deep feel for the code or mechanics. One student suggested that mastering Linux and ROS before enrolling would have made the material easier to manage.
Exams were described as fairly straightforward if students completed the review assignment and period exercises. Preparing adequately for class was considered sufficient for success.
Matrix Methods in Machine Learning covers linear algebraic foundations including SVD, regression, and regularization applied to classification, clustering, and neural networks.
State Space Systems Analysis covers matrix methods for solving state-variable differential equations, focusing on stability, controllability, observability, and controller design.