Complete all courses and requirements listed below unless otherwise indicated.
Fundamental Courses
| Code | Title | Hours |
|---|---|---|
| Complete at least 8 semester hours from the following: | 8 | |
| Introduction to Machine Learning and Pattern Recognition | ||
| Fundamentals of Computer Engineering | ||
| Computer Architecture | ||
| VLSI Design | ||
Options
Complete one of the following options:
Coursework Option
| Code | Title | Hours |
|---|---|---|
| A maximum of three courses may be taken outside of the electrical and computer engineering EECE subject code. | ||
| Concentration Courses | ||
| Complete 16 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. | 16 | |
| Electives | ||
| Complete 8 semester hours from either concentration courses or from other concentrations. | 8 | |
Thesis Option
| Code | Title | Hours |
|---|---|---|
| A maximum of three courses may be taken outside of electrical and computer engineering. | ||
| 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 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 | ||
| Complete 8 semester hours from either concentration courses or from other concentrations. | 8 | |
Optional Co-op Experience
| Code | Title | Hours |
|---|---|---|
Complete the following (students must complete ENCP 6100 to qualify for co-op experience): | ||
| Introduction to Cooperative Education | ||
| Co-op Work Experience | ||
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
Concentration Courses
| Code | Title | Hours |
|---|---|---|
| Reinforcement Learning and Sequential Decision Making | ||
| Robotic Science and Systems | ||
| Theory and Methods in Human Computer Interaction | ||
| 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 ( *Students taking EECE 5641 can't receive credit for CY 5770) | ||
| Data Visualization | ||
| Simulation and Performance Evaluation | ||
| Introduction to Machine Learning and Pattern Recognition | ||
| Parallel Processing for Data Analytics | ||
| 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 (*For MSECE and PhD-BS students only) | ||
| Autonomous Field Robotics | ||
| Applied Probability and Stochastic Processes | ||
| Fundamentals of Computer Engineering | ||
| Introduction to Distributed Intelligence | ||
| 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 | ||
| Analysis and Design of Data Networks | ||
| Advanced Machine Learning | ||
| Advanced Special Topics in Electrical and Computer Engineering (Advances in Deep Learning) | ||
| 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 (Large Language Model Based Dialogue Agent) | ||
| 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 Topics in Electrical and Computer Engineering (Verifiable Machine Learning ) | ||
| Advanced Special Problems in Electrical and Computer Engineering (*For PhD-AE students only) | ||
| Digital Manufacturing | ||
| Graph Theory | ||
| AI Ethics |
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