The master's degree program in electrical and computer engineering offers in-depth coursework within the concentration-choice-related areas. The curriculum is integrated and intensive and is built on state-of-the-art research, taught by faculty who are experts in their areas.
Complete all courses and requirements listed below unless otherwise indicated. A maximum of three courses from subject codes other than EECE (Electrical and Computer Engineering) may be applied to requirements of this program.
Fundamental Courses
Course List | Code | Title | Hours |
| 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
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 |
Course Lists
A maximum of three courses may be taken outside of electrical and computer engineering.
Concentration Courses
Course List | Code | Title | Hours |
| 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 | |
| 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 | |
| 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 | |
| Analysis and Design of Data Networks | |
| 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 Problems in Electrical and Computer Engineering | |
| Master’s Project | |
| Thesis | |
| Reinforcement Learning and Sequential Decision Making | |
| Robotic Science and Systems | |
| Theory and Methods in Human Computer Interaction | |
| Digital Manufacturing | |
| Graph Theory | |
| AI Ethics | |
Elective Courses
Course List | Code | Title | Hours |
| Combinatorial Optimization | |
| Image Processing and Pattern Recognition | |
| Riemannian Optimization | |
| Two Dimensional Signal and Image Processing | |
| Digital Image Processing | |
| Operating Systems: Interface and Implementation | |
| Foundations of Artificial Intelligence | |
| Database Management Systems | |
| Foundations of Software Engineering | |
| Computer Systems | |
| Information Retrieval | |
| Data Mining Techniques | |
| Compilers | |
| Advanced Software Development | |
| Building Scalable Distributed Systems | |
| Privacy, Security, and Usability | |
| Advanced Algorithms | |
| Software Vulnerabilities and Security | |
| Network Security | |
| Essentials of Data Science | |
Program Credit/GPA Requirements
32 total semester hours required (33 with optional co-op)
Minimum 3.000 GPA required
