For program contact information, please visit this website.
The master's degree programs in electrical and computer engineering offer in-depth coursework within the concentration-choice-related areas. The curriculum is integrated and intensive and is built on groundbreaking research, taught by faculty who are experts in their areas.
Excluded Courses for All MSECE Concentrations
Students cannot take excluded courses as part of the MSECE program and may not petition to take these courses, as any petition to take these courses will be automatically rejected. Courses from the following subject areas may not count toward any concentration within the MSECE program: CSYE, DAMG, INFO, TELE. Select CS courses are also excluded from all MSECE concentrations. Please see the program requirements tab and your college administrator for more information.
Complete all courses and requirements listed below unless otherwise indicated.
Fundamental Courses
Course List Code | Title | Hours |
| 8 |
| High-Performance Computing | |
| Fundamentals of Computer Engineering | |
| Computer Architecture | |
| Operating Systems: Interface and Implementation | |
Options
Complete one of the following options:
Coursework Option
Course List Code | Title | Hours |
| 16 |
| 8 |
Thesis Option
Optional Co-op Experience
Course List Code | Title | Hours |
| |
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 |
Program Credit/GPA Requirements
32 total semester hours required (33 with optional co-op)
Minimum 3.000 GPA required
Course Lists
A maximum of three courses may be taken outside of electrical and computer engineering.
Concentration Courses
Course List Code | Title | Hours |
| Database Management Systems | |
| Foundations of Software Engineering | |
| Computer Systems | |
| Compilers | |
| Advanced Software Development | |
| Building Scalable Distributed Systems | |
| Assistive Robotics | |
| High-Performance Computing | |
| Simulation and Performance Evaluation | |
| Special Topics in Electrical and Computer Engineering (Field Programmable Gate Arrays in the Cloud) | |
| Special Topics in Electrical and Computer Engineering (Nano-Computing System Design) | |
| Computer Hardware and System Security | |
| Special Problems in Electrical and Computer Engineering | |
| Fundamentals of Computer Engineering | |
| Computer Architecture | |
| VLSI Design | |
| High-Level Design of Hardware-Software Systems | |
| Operating Systems: Interface and Implementation | |
| Computer Hardware Security | |
| Advanced Special Topics in Electrical and Computer Engineering (Advanced Computer Architecture) | |
| Advanced Special Topics in Electrical and Computer Engineering (Compilers) | |
| Master’s Project | |
| Thesis | |
Elective Courses
Course List Code | Title | Hours |
| Foundations of Artificial Intelligence | |
| Reinforcement Learning and Sequential Decision Making | |
| Robotic Science and Systems | |
| Information Retrieval | |
| Data Mining Techniques | |
| Privacy, Security, and Usability | |
| Advanced Algorithms | |
| Software Vulnerabilities and Security | |
| Network Security | |
| Essentials of Data Science | |
| Combinatorial Optimization | |
| Networked XR Systems | |
| Mobile Robotics | |
| Robotics Sensing and Navigation | |
| Statistical Inference: An Introduction for Engineers and Data Analysts | |
| Reinforcement Learning and Decision Making Under Uncertainty | |
| Image Processing and Pattern Recognition | |
| Computer Vision | |
| Introduction to Software Security | |
| Data Visualization | |
| Introduction to Machine Learning and Pattern Recognition | |
| Parallel Processing for Data Analytics | |
| Special Problems in Electrical and Computer Engineering | |
| Autonomous Field Robotics | |
| Applied Probability and Stochastic Processes | |
| Introduction to Distributed Intelligence | |
| Riemannian Optimization | |
| Two Dimensional Signal and Image Processing | |
| Digital Image Processing | |
| Numerical Optimization Methods | |
| Information Theory | |
| Big Data and Sparsity in Control, Machine Learning, and Optimization | |
| Advanced Computer Vision | |
| Analysis and Design of Data Networks | |
| Advanced Machine Learning | |
| Advanced Special Problems in Electrical and Computer Engineering | |
| Digital Manufacturing | |
| Graph Theory | |
| AI Ethics | |