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 | |
| High-Performance Computing | ||
| Fundamentals of Computer Engineering | ||
| Computer Architecture | ||
| Operating Systems: Interface and Implementation | ||
Options
Complete one of the following options:
Coursework Option
| Code | Title | Hours |
|---|---|---|
| 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. 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 | |
| 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 | |
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 | |
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
| 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
| 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 | ||
EECE 7311 | ||
| Digital Image Processing | ||
| Numerical Optimization Methods | ||
| Information Theory | ||
| Big Data and Sparsity in Control, Machine Learning, and Optimization | ||
| Advanced Computer Vision | ||
EECE 7393 | ||
| Advanced Machine Learning | ||
| Advanced Special Problems in Electrical and Computer Engineering | ||
| Digital Manufacturing | ||
| Graph Theory | ||
| AI Ethics |