The Master of Science in Applied Artificial Intelligence educates students to acquire state-of-the-art knowledge and skills in artificial intelligence and its applications across a broad range of engineering domains. The program prepares students to develop a strong background in understanding the theoretical foundations of artificial intelligence and deep learning, together with the understanding of a variety of engineering applications including intelligent communication networks, autonomous robotics, image processing and computer vision, smart Internet of Things, smart health, information systems security, biomedical and bio-engineering, civil and environmental engineering, mechanical engineering, data engineering, and software engineering. The program prepares students to enter careers in engineering fields that require advanced artificial intelligence knowledge and skills.
In general, a bachelor’s degree in electrical engineering or computer engineering (or a closely related discipline) with a minimum grade point average of 3.0 on a 4.0 scale is required for graduate study in Applied Artificial Intelligence. Outstanding applicants with degrees in other disciplines such as computer science, engineering, management, or mathematics may be admitted subject to demonstration of the technical background expected. Such applicants, as well as applicants with significant career experiences but not satisfying the primary requirements, will be admitted on an individual basis depending on the student’s background. Submission of GRE scores is recommended, but not required. The master’s degree requires completion of a total of 30 hours of credit. Each student must complete the three core courses and must complete the course requirements for one of the artificial intelligence for electrical and computer engineering concentrations. Elective courses are to be chosen from among the CPE and EE numbered graduate courses in this catalog. Under special circumstances, an elective course not in the CPE or EE numbered courses may be taken, with the approval of the student’s academic advisor. A maximum of two elective courses not listed in the ECE program may be used for the master’s degree with approval of the academic advisor.
Master of Science in Applied Artificial Intelligence Requirements
The Master of Science in Applied Artificial Intelligence is a 30-credit degree program. Students seeking a Master of Science (MS) in Applied Artificial Intelligence are required to complete:
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One (1) mathematical foundation course
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Four (4) core courses in their majors/programs
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Three (3) concentration courses in a chosen concentration
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Three (3) credits of project work and Three (3) credits elective course or Six (6) credits thesis
Mathematical Foundation Courses
Students are required to select one mathematical foundation course from the list below:
| EE 602 | Analytical Methods in Electrical Engineering | 3 |
| | Or | |
| EE 605 | Probability and Stochastic Processes I | 3 |
Core Courses
Students are required to select four core courses from the list below:
| AAI 695 | Applied Machine Learning | 3 |
| AAI 646 | Pattern Recognition and Classification | 3 |
| EE 608 | Applied Modeling and Optimization | 3 |
| AAI 627 | Data Acquisition, Modeling and Analysis: Big Data Analytics | 3 |
| AAI 628 | Data Acquisition, Modeling and Analysis: Deep Learning | 3 |
| AAI 672 | Applied Game Theory and Evolutionary Algorithms | 3 |
Electrical Engineering Concentration Course Options
The Electrical Engineering concentration will provide students with fundamental knowledge and background on electrical engineering and will allow students to apply Artificial Intelligence algorithms to a wide array of topics including but not limited to communications, smart grids, and digital systems design.
Students who select the Electrical Engineering concentration are required to select three concentration courses from the list below:
| EE 548 | Digital Signal Processing | 3 |
| EE 575 | Introduction to Control Theory | 3 |
| EE 582 | Wireless Networking: Architectures, Protocols and Standards | 3 |
| EE 603 | Linear System Theory | 3 |
| EE 608 | Applied Modeling and Optimization | 3 |
| EE 609 | Communication Theory | 3 |
Computer Engineering Concentration Course Options
The Computer Engineering concentration will provide students with fundamental knowledge and background on computer engineering and will allow students to apply Artificial Intelligence algorithms to a wide array of topics including but not limited to communications, networking, information networks, image processing and computer vision, security, real time and embedded systems, and robotics and control.
Students who select the Computer Engineering concentration are required to select three concentration courses from the list below:
| CPE 517 | Digital and Computer Systems Architecture | 3 |
| CPE 555 | Real-Time and Embedded Systems | 3 |
| CPE 593 | Applied Data Structures and Algorithms | 3 |
| CPE 679 | Computer and Information Networks | 3 |
| CPE 690 | Introduction to VLSI Design | 3 |
| EE 608 | Applied Modeling and Optimization | 3 |
Data Engineering Concentration Course Options
The Data Engineering concentration will provide students with fundamental knowledge and background necessary for analysis, management and classification of Big Data for a variety of application domains, including information systems security, and data management for web applications.
Students who select the Data Engineering concentration are required to select three concentration courses from the list below:
| AAI 551 | Engineering Programming: Python | 3 |
| AAI 627 | Data Acquisition, Modeling and Analysis: Big Data Analytics | 3 |
| AAI 628 | Data Acquisition, Modeling and Analysis: Deep Learning | 3 |
| CPE 593 | Applied Data Structures and Algorithms | 3 |
Software Engineering Concentration Course Options
The Software Engineering concentration will provide students with fundamental knowledge and background on software development with an emphasis on developing software for AI applications.
Students who select the Software Engineering concentration are required to select three concentration courses from the list below:
| AAI 551 | Engineering Programming: Python | 3 |
| EE 552 | Engineering Programming: Java | 3 |
| EE 553 | Engineering Programming: C++ | 3 |
| AAI 627 | Data Acquisition, Modeling and Analysis: Big Data Analytics | 3 |
| AAI 628 | Data Acquisition, Modeling and Analysis: Deep Learning | 3 |
| CPE 593 | Applied Data Structures and Algorithms | 3 |
| CPE 810 | Special Topics in ComputerEngineering | 3-6 |
Biomedical Engineering Concentration Course Options
The Biomedical Engineering concentration will provide students with the engineering skills needed to extract and process biomedical signals, compute and analyze medical data, and build new user friendly healthcare applications.
Students who select the Biomedical Engineering concentration are required to select three concentration courses from the list below:
| BME 810 | Special Topics in Biomedical Engineering | 1-3 |
| BME 558 | Introduction to Brain-Machine Interfaces | 3 |
| | | |
| BME 504 | Medical Instrumentation and Imaging | 3 |
| | Or | |
| CPE 585 | Medical Instrumentation and Imaging | 3 |
Systems Biology Concentration Course Options
The Systems Biology concentration will provide students with sufficient knowledge and background to perform mathematical and computational modeling of complex biological systems in a wide range of areas including genetics and bioinformatics.
Students who select the Systems Biology concentration are required to select three concentration courses from the list below:
| BIO 687 | Molecular Genetics | 3 |
| CH 580 | Biochemistry I - Cellular Metabolism and Regulation | 3 |
| BIO 668 | Computational Biology | 3 |
Mechanical Engineering Concentration Course Options
The Mechanical Engineering concentration will provide students with fundamental knowledge and background in design and manufacturing to perform mathematical and computational modeling of complex mechanical systems including but not limited to robotics.
Students who select the Mechanical Engineering concentration are required to select three concentration courses from the list below:
| ME 598 | Introduction to Robotics | 3 |
| ME 621 | Introduction to Modern Control Engineering | 3 |
| ME 644 | Computer-Integrated Design and Manufacturing | 3 |
Artificial Intelligence in Design and Construction Concentration Course Options
Students who complete this concentration will acquire a practical grounding in artificial intelligence as applied to the Design and Construction Industry, including its potential to transform organizations by increasing productivity and efficiency while enhancing safety and profitability. By evaluating current trends and applications, students will be able to investigate and conclude the capital investments and project-specific feasibility necessary to incorporate AI throughout the design and construction process.
Students who select the Artificial Intelligence in Design and Construction concentration are required to select three concentration courses from the list below:
| OE 511 | Urban Oceanography | 3 |
| CM 521 | Leading Construction Organizations | 3 |
| CM 530 | Strategic Responses to Cyclical Environments | 3 |
| CM 560 | Sustainable Design | 3 |
Project or Thesis
Students in the Master of Science program are required to complete:
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Project Track: 3 credit project course (800 course) and a 3 credit elective course at the 500 or 600 level. Students who enroll in the 3 credit project course (800 course) are required to enroll in the 0-credit co-requisite research seminar course, EE 820. The 3 credit elective course can be any graduate level course at the 500 or 600 levels within the Department of Electrical and Computer Engineering. Elective courses that are taken outside of the department require approval by the faculty advisor.
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