About This Program

Electrical and Computer Engineering Department, College of Engineering and Applied Sciences Degree Awarded: Master of Science in Engineering Artificial Intelligence Chairperson: Pamela Abshire, Light Engineering Building 273 (631) 632-9376 Graduate Program Director: Fan Ye, Light Engineering Building 143 (631) 632-9376 Department Manager: Chantalle McKim, Light Engineering Building 273 (631) 632-8420 Graduate Program Coordinator: Ashley Cimato, Light Engineering Building 267A (631) 632-8401 Department Coordinator: Adam Ortiz, Light Engineering Building 267 (631) 631-8415 Department Website Program Website The Master of Science in Engineering of Artificial Intelligence (EAI) prepares specialists with comprehensive knowledge in all areas of this new disruptive and revolutionary technology. The program provides interdisciplinary foundations and practical experience in algorithms, sensors, hardware, control, and applications. The program consists of a three-semester course sequence which covers the fundamentals of Artificial Intelligence, probabilistic reasoning, machine learning, deep learning algorithms, sensor electronics, digital systems design and acceleration hardware, control theory and practice, convex optimization, natural language processing, and computer vision and applications in mobile, health, and other domains. The holistic nature of the program allows students to specialize in any sub-field of Artificial Intelligence (AI) and solve real world problems, many of...

Degree Requirements

Subarea: Foundations

Code	Course Title	Credits
ESE 503	Stochastic Systems	3
ESE 561	Theory of Artificial Intelligence	3
Total Subarea: Foundations		6 credits

Subarea: Methods

Code	Course Title	Credits
ESE 577	Deep Learning Algorithms and Software	3
ESE 588	Fundamentals of Machine Learning	3
Total Subarea: Methods		6 credits

Subarea: Applications

Select one of the following:

Code	Course Title	Credits
ESE 564	Artificial Intelligence for Robotics	3
ESE 589	Learning Systems for Engineering Applications	3
ESE 590	Practical Machine Learning and Artificial Intelligence	3
Total Subarea: Applications		3 credits

Subarea: Hardware

Select one of the following:

Code	Course Title	Credits
ESE 507	Advanced Digital System Design and Generation	3
ESE 525	Moden Sensors in Artificial Intelligence Applications	3
ESE 587	Hardware Architectures for Deep Learning	3
Total Subarea: Hardware		3 credits

Electives

3 elective credits are required for the Thesis Option 6 elective credits are required for the non-Thesis Option Also required: Any ESE or non-ESE course not listed above that has been approved by Graduate Program Director (3 credit max.; approval must be obtained before enrollment). Non-regular courses cannot be used as a substitution course for the elective requirement.; * ESE 670 - Topics in Electrical Sciences (3 credits) is a course with variable content, and it can be approved by the Graduate Program Director as an elective course for the EAI MS degree when the course topic corresponds to the program area..

Code	Course Title	Credits
ESE 502	Linear Systems	3
ESE 507	Advanced Digital System Design and Generation	3
ESE 525	Moden Sensors in Artificial Intelligence Applications	3
ESE 533	Convex Optimization and Engineering Applications	3
ESE 537	Mobile Sensing Systems & Applications	3
ESE 543	Mobile Cloud Computing	3
ESE 558	Digital Image Processing I	3
ESE 562	AI Driven Smart Grids	3
ESE 564	Artificial Intelligence for Robotics	3
ESE 568	Computer and Robot Vision	3
ESE 587	Hardware Architectures for Deep Learning	3
ESE 589	Learning Systems for Engineering Applications	3
ESE 590	Practical Machine Learning and Artificial Intelligence	3
ESE 592	Distributed Computation, Control and Learning Over Networks	3
ESE 670	Topics in Electrical Sciences	3
AMS 580	Statistical Learning	3
MEC 529	Introduction to Robotics: Theory and Applications	3
CSE 538	Natural Language Processing	3

Thesis Option

Industrial Experience: at least 1 credit but no more than 3 credits. Please note: A student may request CPT more than once. However, please note that each time you submit a request for a particular semester, you will need to request CPT for that semester as well. Please check with Visa and Immigration Services (VIS) for more information regarding this process. In exceptional circumstances, the Graduate Program Director can approve a replacement of ESE 597 credits by ESE 599 (Research for MS students) or ESE 698 (Practicum in teaching) credits. Students who choose to take one credit of industrial experience may consult with their advisor on how to complete the remaining two credits for...

Code	Course Title	Credits
ESE 597	Practicum in Engineering	3
ESE 599	Research Master’s students	1-12
ESE 698	Practicum in Teaching	1-3

Non-Thesis Option

Industrial Experience: 3 credits. To meet the 30-credit minimum for the program, non-thesis students may take one of the following (maximum 3 credits per course): Research Experience (maximum 3 credits) Teaching Experience (maximum 3 credits) Additional regular course (maximum 3 credits): any course listed under “common requirements,” not already used to fulfill another requirement OR Any ESE or non-ESE course not listed above but approved by Graduate Program Director (3 credit maximum; approval must be obtained before enrollment).

Code	Course Title	Credits
ESE 597	Practicum in Engineering	3
ESE 599	Research Master’s students	1-12
ESE 698	Practicum in Teaching	1-3

Click a course code or title to read the full course description.

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