Complete all courses and requirements listed below unless otherwise indicated.

Fundamental Courses

Complete at least 8 semester hours from the following:8
Robotics Sensing and Navigation
Introduction to Machine Learning and Pattern Recognition
Fundamentals of Computer Engineering
Computer Architecture

Options

Complete one of the following options:

Coursework Option

Concentration Courses
Complete 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
Complete 8 semester hours from either concentration courses or from other concentrations. 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 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
Complete 8 semester hours from either concentration courses or from other concentrations. 8

Option Co-op Experience

Complete the following (students must complete ENCP 6100 to qualify for co-op experience):
Introduction to Cooperative Education
Co-op Work Experience
Co-op Work Experience - Half-Time
Co-op Work Experience Abroad - Half-Time
Co-op Work Experience Abroad

Course Lists

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

Concentration Courses  

Foundations of Artificial Intelligence
Information Retrieval
Data Mining Techniques
Advanced Algorithms
Essentials of Data Science
Topics in Data Science
Combinatorial Optimization
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 for Dynamical Systems )
Special Topics in Electrical and Computer Engineering (Visual Sensing & Computing Co-Design Edge Machine Perception)
Special Problems in Electrical and Computer Engineering (*For MSECE and PhD-BS students only)
Autonomous Field Robotics
Applied Probability and Stochastic Processes
Fundamentals of Computer Engineering
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
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-Scale Learning-Enabled Systems)
Advanced Special Topics in Electrical and Computer Engineering (Verifiable Machine Learning )
Advanced Special Problems in Electrical and Computer Engineering (*For PhD-AE students only)
Graph Theory

Excluded Courses for All MSECE Concentrations

 Please see your college administrator for more information.

Courses from the following subject areas may not count toward any concentration within the MSECE program:
CSYE, DAMG, INFO, TELE
The following CS courses may not count toward any concentration within the MSECE program:
Programming Design Paradigm
Pattern Recognition and Computer Vision
Computer/Human Interaction
Mobile Application Development
Web Development
Fundamentals of Computer Networking
Algorithms
Machine Learning
Empirical Research Methods

Program Credit/GPA Requirements

32 total semester hours required (33 with optional co-op)

Minimum 3.000 GPA required