Beginning with students admitted in the fall of 2026, all students pursuing on-ground master's degree programs must complete a university-level experiential learning requirement. This requirement may be fulfilled through the curriculum, co-op, or other work- or industry-integrated learning experiences.
Complete all courses and requirements listed below unless otherwise indicated. A maximum of three courses from subject codes other than EECE may be applied to requirements of this program.
Fundamental Courses
| Code | Title | Hours |
|---|---|---|
| Complete at least 8 semester hours from the following: | 8 | |
| EECE 5644 | Introduction to Machine Learning and Pattern Recognition | 4 |
| EECE 7205 | Fundamentals of Computer Engineering | 4 |
| EECE 7352 | Computer Architecture | 4 |
| EECE 7353 | VLSI Design | 4 |
Options
Complete one of the following options:
Coursework Option
| Code | Title | Hours |
|---|---|---|
| Concentration Courses | ||
| Complete a minimum of 16 semester hours from the concentration course list below. | 16 | |
| Any fundamental course not used to meet the fundamental course requirement can be used toward the concentration course requirement. | ||
| Elective Courses | ||
| Students may complete a maximum of 8 semester hours from either the concentration course list or a maximum of 8 semester hours from the elective course list. | 8 | |
Project Option
| Code | Title | Hours |
|---|---|---|
| Project | ||
| Master’s Project | ||
| Concentration Courses | ||
| Complete 12 semester hours from the concentration course list below. Any fundamental course not used to meet the fundamental course requirement can be used toward the concentration course requirement. | 12 | |
| Electives | ||
| Complete 8 semester hours from either concentration courses or from other concentrations. | 8 | |
Thesis Option
| Code | Title | Hours |
|---|---|---|
| Thesis | ||
| EECE 7945 | Master’s Project | 4 |
| EECE 7990 | Thesis | 4 |
| In addition to completing the thesis course, students must successfully complete the thesis submission process, including securing committee and Graduate School of Engineering signatures and submission of an electronic copy of their MS thesis to ProQuest. | ||
| Concentration Courses | ||
| Complete a minimum of 8 semester hours from the concentration course list below. Any fundamental course not used to meet the fundamental course requirement can be used toward the concentration course requirement. | 8 | |
| Electives | ||
| Students may complete a maximum of 8 semester hours from either the concentration course list or a maximum of 8 semester hours from the elective course list. | 8 | |
Optional Co-op Experience
| Code | Title | Hours |
|---|---|---|
| Complete the following (students must complete ENCP 6100 to qualify for co-op experience): | ||
| ENCP 6100 | Introduction to Cooperative Education | 1 |
| ENCP 6964 | Co-op Work Experience | 0 |
| or ENCP 6954 | Co-op Work Experience - Half-Time | |
| or ENCP 6955 | Co-op Work Experience Abroad - Half-Time | |
| or ENCP 6965 | Co-op Work Experience Abroad | |
Course Lists
A maximum of three courses may be taken outside of electrical and computer engineering.
Concentration Courses
| Code | Title | Hours |
|---|---|---|
| Reinforcement Learning and Sequential Decision Making | ||
| Robotic Science and Systems | ||
| Theory and Methods in Human Computer Interaction | ||
| Networked XR Systems | ||
| Mobile Robotics | ||
| Assistive Robotics | ||
| Robotics Sensing and Navigation | ||
| Statistical Inference: An Introduction for Engineers and Data Analysts | ||
| Reinforcement Learning and Decision Making Under Uncertainty | ||
| Computer Vision | ||
| High-Performance Computing | ||
| Introduction to Software Security | ||
| Data Visualization | ||
| Simulation and Performance Evaluation | ||
| Introduction to Machine Learning and Pattern Recognition | ||
| Parallel Processing for Data Analytics | ||
| Signal Processing for Global Navigation Satellite Systems | ||
| Special Topics in Electrical and Computer Engineering (Cyber-Physical Security of IoT Systems in the Age of AI) | ||
| Special Topics in Electrical and Computer Engineering (Field Programming Gate Arrays in the Cloud) | ||
| Special Topics in Electrical and Computer Engineering (Formal Methods for Dynamical Systems) | ||
| Special Topics in Electrical and Computer Engineering (Visual Sensing & Computing Co-Design Edge Machine Perception) | ||
| Computer Hardware and System Security | ||
| Special Problems in Electrical and Computer Engineering | ||
| Autonomous Field Robotics | ||
| Applied Probability and Stochastic Processes | ||
| Fundamentals of Computer Engineering | ||
| Introduction to Distributed Intelligence | ||
| Verifiable Machine Learning | ||
| Numerical Optimization Methods | ||
| Information Theory | ||
| Big Data and Sparsity in Control, Machine Learning, and Optimization | ||
| Probabilistic System Modeling and Analysis | ||
| Computer Architecture | ||
| VLSI Design | ||
| High-Level Design of Hardware-Software Systems | ||
| Advanced Computer Vision | ||
| Computer Hardware Security | ||
| Advanced Machine Learning | ||
| Advanced Special Topics in Electrical and Computer Engineering (Advances in Deep Learning) | ||
| Advanced Special Topics in Electrical and Computer Engineering (Bayesian Filtering and Tracking) | ||
| Advanced Special Topics in Electrical and Computer Engineering (Deep Learning Embedded Systems) | ||
| Advanced Special Topics in Electrical and Computer Engineering (Flexible Robotics) | ||
| Advanced Special Topics in Electrical and Computer Engineering (Human Centered Computing) | ||
| Advanced Special Topics in Electrical and Computer Engineering (Legged Robotics) | ||
| Advanced Special Topics in Electrical and Computer Engineering (Machine Learning with Small Data) | ||
| Advanced Special Problems in Electrical and Computer Engineering | ||
| Digital Manufacturing | ||
| Graph Theory | ||
| AI Ethics |
Elective Courses
| Code | Title | Hours |
|---|---|---|
| The following course codes at the 5xxx level and above are eligible as electives: | ||
CS, CY EECE, IE, ME (excepting excluded courses) | ||
Excluded Courses for All MSECE Concentrations
Please see your college administrator for more information.
| Code | Title | Hours |
|---|---|---|
| Courses from the following subject areas may not count toward any concentration within the MSECE program: | ||
CSYE, DAMG, INFO, TELE | ||
| The following CS courses may not count toward any concentration within the MSECE program: | ||
| Programming Design Paradigm | ||
| Pattern Recognition and Computer Vision | ||
| Computer/Human Interaction | ||
| Mobile Application Development | ||
| Web Development | ||
| Fundamentals of Computer Networking | ||
| Algorithms | ||
| Machine Learning | ||
| Empirical Research Methods | ||
Program Credit/GPA Requirements
32 total semester hours required (33 with optional co-op)
Minimum 3.000 GPA required