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Bachelor’s degree in engineering, or a closely related field, from an accredited college or university.

Those seeking admission to the graduate certificate program without such a degree may petition to have their baccalaureate degree and professional experience accepted as a substitute.

Students who earn this Graduate Certificate, and later seek to transfer the course credit towards the Master of Science in Electrical Engineering, must earn a minimum 3.0 GPA for the certificate, and only classes with “B” or better will transfer for credit.

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EE 676/CPE 674/ME 676/IE 676/CEE 676: AI: Applied Fuzzy Logic
This course covers the fundamentals of fuzzy logic theory and its applications. In this course students will learn to analyze crisp and fuzzy sets, fuzzy propositional calculus, predicate logic, fuzzy logic, fuzzy rule-based expert systems, and will learn to apply fuzzy logic theory to a variety of practical applications. Students will also learn to use MATLAB computational software to understand new concepts and to perform and implement fuzzy logic rules and systems.

ME 671/EE 671/CPE 671/IE 671/CEE 671: AI: Machine Learning – Concepts
This course focuses on AI concepts such as Data Exploration, Single and Multivariate Parametric and Non-Parametric methods of regression and classification tasks. Students will learn the theory that underlies these algorithms and implement them using popular machine learning packages such as Python with scikit-learn and MATLAB. During the final project, students will implement multiple algorithms and learn how to select the best algorithm with the optimized hyperparameters.

EE 670/CPE 673/ME 670/IE 670/CEE 673: AI: Applied Neural Networks and Machine Learning
This course is a study of the basic concepts of neural networks and its application in engineering. In this course students will learn the single layer and multilayer neural networks architectures, linear and nonlinear activation functions, and will analyze and implement Hebbian, Hopfield, Perceptron, Widrow-Hoff, aeronautics, delta, and backpropagation, and machine learning algorithms and techniques with ample practical applications. Students will also learn to use MATLAB computational software to understand new concepts and to perform and implement neural network and machine learning algorithms.

ME 672/EE 672/CPE 672/IE 672/CEE 674: AI: Machine Learning – Applications
This course focuses on Artificial Intelligence application packages such as Data exploration, Natural Language Processing, Support Vector Machine, Reinforcement Learning, Artificial Neural Networks (ANNs) and Computer Vision and Deep Learning. Students will learn the theory and applications of a variety of algorithms. These algorithms will be implemented using Python and MATLAB software. As the final project, students will apply a combination of algorithms to a specific application and develop an end to end solution.

As a small but robust program, the faculty get to know each individual student, which helps them customize your educational experience to suit your needs and goals. All members of the full-time faculty have doctoral degrees with research expertise in areas such as RF/Wireless systems, digital signal/image processing and communications, embedded systems, robotics (mechatronics), integrated circuit design, nanotechnology, photonics, software and systems engineering, microprocessors. Our faculty also have industrial experience that enhances their teaching as well as industrial contacts that are leveraged in obtaining internships, research projects, and jobs for our students.

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