Complete all courses and requirements listed below unless otherwise indicated.

Fundamental Courses

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

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 

Thesis
EECE 7945Master’s Project4
EECE 7990Thesis4
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

Complete the following. Students must complete ENCP 6100 to qualify for co-op experience:
ENCP 6100Introduction to Cooperative Education1
ENCP 6964Co-op Work Experience0
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

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

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