Artificial Intelligence: Computer Engineering, MS
Master's in Artificial Intelligence
The Master of Science (MS) in Artificial Intelligence – Computer Engineering CpE Track) prepares students to integrate and advance AI methodologies within the domain of computer engineering. The curriculum emphasizes the design and implementation of intelligent systems, smart technologies, and autonomous decision-making processes through machine learning, robotics, computer vision, and deep neural networks.
This 33-credit-hour program equips graduates with the analytical and computational expertise required for emerging careers in AI driven system design, advanced algorithm development, and intelligent automation across industries such as energy, defense, manufacturing, and healthcare. The 30-credit-hour program with a thesis option is available soon.
Curriculum
The Joint MS in Artificial Intelligence (Computer Engineering Track) offers a flexible pathway tailored to your pace and goals. The program requires completion of 11 courses (33 credit hours) and can be pursued full-time or part-time, with guidance from the Graduate
Coordinator to help you plan your degree.
Core Courses
(9 hours)
- CS 5273 - Artificial Intelligence I
- CS 5783 - Machine Learning
- ECEN 5733 - Neural Networks
- ECEN 5773 - Intelligent Systems
Track Required Courses
(6 hours)
- ECEN 5513 - Stochastic Systems
- ECEN 5743 - Deep Learning
Elective Courses
(18 hours)
- CS 5793 - Artificial Intelligence II
- CS 5683 - Big Data Analytics
- ECEN 5283 - Computer Vision
- ECEN 5243 Advanced Mobile Robotics
- ECEN 5453 - Engineering Applications of Artificial Intelligence
- ECEN 5793 - Digital Image Processing
Any core course not selected
ECEN 5080 - Special Topics (up to 9 hrs)
Mobile Robotics, AI in Engineering, and Applied Method with Python for Engineers
*At least 12 hours of elective courses must be from Computer Engineering (ECEN).
Why a Master's in AI at OSU?
- Strong interdisciplinary collaboration between the School of Electrical and Computer Engineering and the School of Computer Science
- Jointly supported by a dynamic team of research-active and teaching-focused faculty dedicated to academic excellence and student success
- Access to state-of-the-art computing resources and modern laboratory facilities
- Flexible learning options with both in-person and online delivery formats
- Opportunities to engage in faculty-led research in Artificial Intelligence, Energy Systems, and Robotics
- Extensive industry partnerships providing pathways for internships, mentorship, and professional growth
Career Pathways
Graduates are prepared for careers such as:
- AI and Machine Learning Engineer
- Embedded AI/Edge AI Engineer
- Data Scientist/Data Engineer
- Signal Processing & AI Engineering
- Computer Visio/Robotics Engineer
- AI Engineer for Energy & Power Systems
- AI Hardware/Accelerator Engineer
