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
| 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. Any fundamental course not used to meet the fundamental course requirement can be used toward the concentration course requirement. | 16 | |
| 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 | |
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 |
|---|---|---|
| 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 | ||
EECE 7393 | ||
| 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
| Code | Title | Hours |
|---|---|---|
| Combinatorial Optimization | ||
| Image Processing and Pattern Recognition | ||
| Riemannian Optimization | ||
EECE 7311 | ||
| 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