Complete all courses and requirements listed below unless otherwise indicated. A maximum of three courses outside of the EECE (Electrical and Computer Engineering) subject code may be applied to requirements of this program.
Fundamental Courses
| Code | Title | Hours |
|---|---|---|
| Complete at least 8 semester hours from the following: | 8 | |
| EECE 5554 | Robotics Sensing and Navigation | 4 |
| EECE 5644 | Introduction to Machine Learning and Pattern Recognition | 4 |
| EECE 7205 | Fundamentals of Computer Engineering | 4 |
| EECE 7352 | Computer Architecture | 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 | |
| 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 |
|---|---|---|
| Foundations of Artificial Intelligence | ||
| Information Retrieval | ||
| Data Mining Techniques | ||
| Advanced Algorithms | ||
| Essentials of Data Science | ||
| Topics in 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 | ||
| High-Performance Computing | ||
| Data Visualization | ||
| Introduction to Machine Learning and Pattern Recognition | ||
| Parallel Processing for Data Analytics | ||
| Special Topics in Electrical and Computer Engineering (Formal Methods of Dynamical Systems) | ||
| Special Topics in Electrical and Computer Engineering (Visual Sensing & Computing Co-Design Edge Machine Perception) | ||
| Special Problems in Electrical and Computer Engineering | ||
| Autonomous Field Robotics | ||
| Applied Probability and Stochastic Processes | ||
| Fundamentals of Computer Engineering | ||
| 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 | ||
| Probabilistic System Modeling and Analysis | ||
| Computer Architecture | ||
| Advanced Computer Vision | ||
| 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 for Embedded Systems) | ||
| Advanced Special Topics in Electrical and Computer Engineering (Distributed Intelligence) | ||
| 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 Agents) | ||
| Advanced Special Topics in Electrical and Computer Engineering (Machine Learning with Small Data) | ||
| Advanced Special Topics in Electrical and Computer Engineering (Security in Large-Scaled Learning Enabled Systems) | ||
| Advanced Special Problems in Electrical and Computer Engineering | ||
| Master’s Project | ||
| Thesis | ||
| Graph Theory |
Elective Courses
| Code | Title | Hours |
|---|---|---|
| Reinforcement Learning and Sequential Decision Making | ||
| Database Management Systems | ||
| Robotic Science and Systems | ||
| Foundations of Software Engineering | ||
| Computer Systems | ||
| Compilers | ||
| Advanced Software Development | ||
| Building Scalable Distributed Systems | ||
| Privacy, Security, and Usability | ||
| Theory and Methods in Human Computer Interaction | ||
| Software Vulnerabilities and Security | ||
| Network Security | ||
| Assistive Robotics | ||
| Introduction to Software Security | ||
| Simulation and Performance Evaluation | ||
| Computer Hardware and System Security | ||
| VLSI Design | ||
| High-Level Design of Hardware-Software Systems | ||
| Operating Systems: Interface and Implementation | ||
| Computer Hardware Security | ||
EECE 7393 | ||
| Digital Manufacturing | ||
| AI Ethics |