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

Complete at least 8 semester hours from the following:8
EECE 5644Introduction to Machine Learning and Pattern Recognition4
EECE 7205Fundamentals of Computer Engineering4
EECE 7352Computer Architecture4
EECE 7353VLSI Design4

Options

Complete one of the following options:

Coursework Option

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

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

Course Lists 

A maximum of three courses may be taken outside of electrical and computer engineering. 

Concentration Courses

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

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