Master of Engineering in Artificial Intelligence for Computer Vision and Control
AI has become a valuable and important catalyst for other technologies such as the Internet of Things and Cyber Physical Systems. AI is also considered as the engine that powers several truly ground-breaking Computer Vision, Control and Cybernetic applications such as autonomous cars, robotic personal assistants and automated manufacturing. The Master of Engineering in Artificial Intelligence, Computer Vision and Control degree is intended to provide interested students maximum exposure towards the very fast evolving AI technologies, machine learning, and methods while particularly targeting Electrical and Computer Engineering topics such as computer vision, medical image diagnosis, power system distribution, robotics and automation. Today, all major new technology products inherit a layer of artificial intelligence unit for self-learning and adaptability. Depending on the application or platform, this AI component can be used for speech recognition, face recognition or context-aware human-device interactions. The wide use of these AI technologies resulted in a major spike in the demand for engineers who are trained in this dynamic and fast-growing exciting field. Through the Master of Engineering in Artificial Intelligence, Computer Vision and Control students will be ready to overcome challenges in the field of core AI framework, signal & image processing and computer vision, control systems, embedded systems, integrated circuits and VLSI including neuromorphic computing, network, communication and information systems, power systems and robotics.
Minimum Credits Required | 30 |
Maximum 400-Level Credit | 12 |
Minimum 500-Level+ Credit | 18 |
Maximum 700-Level Credit | 4 |
Maximum Transfer Credit | 9 |
Code | Title | Credit Hours |
---|---|---|
Required Courses | (15) | |
Select minimum 5 courses from the following: | 15 | |
Digital Signal Processing I | 3 | |
or ECE 569 | Digital Signal Processing II | |
Control Systems | 3 | |
or ECE 533 | Robust Control | |
Artificial Intelligence and Edge Computing | 3 | |
Internet of Things and Cyber Physical Systems | 3 | |
Artificial Intelligence in Smart Grid | 3 | |
Computer Vision and Image Processing (required) | 3 | |
Machine and Deep Learning (required) | 3 | |
Secure Machine Learning Design and Applications | 3 | |
Cloud Computing and Cloud Native Systems | 3 | |
Data Science for Engineers | 3 | |
Object-Oriented Programming and Machine Learning (required) | 3 | |
Special Problems (Artificial Intelligence, Computer Vision and Control) | 1-3 | |
Signal and Image Processing Elective | (3) | |
Select a minimum 1 course from the following: | 3 | |
Digital Signal Processing I | 3 | |
Image Processing | 3 | |
Video Communications | 3 | |
Analysis of Random Signals | 3 | |
Artificial Intelligence in Smart Grid | 3 | |
Computer Vision and Image Processing | 3 | |
Machine and Deep Learning | 3 | |
Statistical Signal Processing | 3 | |
Digital Speech Processing | 3 | |
Digital Signal Processing II | 3 | |
Secure Machine Learning Design and Applications | 3 | |
Computer Engineering Elective | (3) | |
Select a minimum 1 course from the following: | 3 | |
Introduction to Computer Networks | 3 | |
Smart and Connected Embedded System Design | 4 | |
Artificial Intelligence and Edge Computing | 3 | |
Internet of Things and Cyber Physical Systems | 3 | |
Modern Wireless Network Protocols and Standards | 3 | |
Computer Cyber Security | 3 | |
Information Theory and Applications | 3 | |
Application Software Design | 3 | |
Communications Networks Performance Analysis | 3 | |
Computer Network Security | 3 | |
Modern Internet Technologies | 3 | |
Cloud Computing and Cloud Native Systems | 3 | |
Data Science for Engineers | 3 | |
Computer Organization and Design | 3 | |
Hardware Security and Advanced Computer Architectures | 3 | |
Hardware/Software Codesign | 3 | |
Object-Oriented Programming and Machine Learning | 3 | |
Power and Control Engineering Elective | (3) | |
Select a minimum 1 course from the following: | 3 | |
Power Electronics | 4 | |
Control Systems | 3 | |
Applied Optimization for Engineers | 3 | |
Hybrid Electric Vehicle Drives | 3 | |
Robust Control | 3 | |
Next Generation Smart Grid | 3 | |
Motion Control Systems Dynamics | 3 | |
Power Electronic Dynamics and Control | 3 | |
Advanced Power Electronics | 3 | |
Adjustable Speed Drives | 3 | |
Power Market Operations | 3 | |
Fault-Tolerant Power Systems | 3 | |
Power System Reliability | 3 | |
Power Systems Dynamics and Stability | 3 | |
Control and Operation of Electric Power Systems | 3 | |
Operations and Planning and Distributed Power Grid | 3 | |
Elements of Sustainable Energy | 3 | |
Elements of Smart Grid | 3 | |
Microgrid Design and Operation | 3 | |
Elective Courses | (6) | |
The remaining elective courses may be chosen from any of the listed core or elective options, provided that those courses were not already used to satisfy another degree requirement. | 6 | |
Total Credit Hours | 30 |