Degrees & ProgramsEngineeringMEng in Electrical EngineeringCurriculum at a Glance

Curriculum: Master of Engineering in Electrical Engineering

July 01, 2021
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MEng in Electrical Engineering

curriculum icon Curriculum at a Glance

The Master of Engineering in Electrical Engineering requires the student to successfully complete a minimum of 30 semester credit hours of specific coursework.

EECE 6023C
4 Semester Credits

Security & Trust for Cyberphysical Systems

Cyber-physical systems integrate computation, communication, and physical processes. They interact with the world in real time. With the growth of powerful networks and the Internet of Things (IoT), these systems are charged with performing many tasks for which reliability, safety, and security are critical. This course focuses on the development of trusted cyber-physical systems, which can be counted on to perform their tasks at defined levels of reliability, safety, and security.
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MECH 6079C
3 Semester Credits

Intro to Additive Manufacturing

In this course students will be introduced to additive manufacturing technology or, as it’s more widely known, 3D printing. This will include addressing the benefits and drawbacks of additive as compared to subtractive manufacturing, the various modalities and materials available, the different components of the production cycle, part and support structure design considerations, cutting edge AM techniques, and monitoring, inspection, and surface modification techniques. This lecture-based content will be complemented by two main hands-on project components using fused deposition modeling (FDM) and laser powder bed fusion (L-PBF) techniques where students will design and print parts.
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EECE 6051
3 Semester Credits

Introduction to Sensors

Introduction to sensor principles, design and implementation, signal conditioning, instrumentation and networking, and applications in engineering practice. Designed for CEAS students and students with STEM backgrounds.
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ENGR 6032
3 Semester Credits

Industry 4.0 Domains of Knowledge

Industry 4.0 describes the evolution of industry toward inter-connectivity, automation, machine learning, and basing decisions on real-time data acquisition. The course provides students a broad understanding of the industrial internet of things (IIOT) which encompasses physical production and operations with smart digital technology, machine learning, and big data analysis to create a connected ecosystem for organizations that focus on manufacturing and supply chain management.
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ENGR 6010
3 Semester Credits

Effectiveness in Technical Organizations

This course examines the non-technical factors that enable engineers and other technical professionals to maximize their contribution to organizational effectiveness. The course covers communication processes and impediments to effective communication including written communication, presentations, and meeting facilitation. Models of motivation as regards technical professionals are presented and their application to the work setting are examined. Leadership models and the interaction of leaders and followers are also presented. Conflict management and appropriate methods for constructively dealing with this are discussed. Students develop personal development plans for continued learning and performance improvement.
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EECE 6005
3 Semester Credits

Intelligent Industrial Controllers

Study the current technology and use of intelligent industrial controllers utilized in electric energy, manufacturing, material handling / processing, mass transit, and other industrial plants. Selection and programming of Programmable Automation Controllers (PACs), Programmable Logic Controllers (PLCs), and Distributed Control Systems (DACs) are covered.
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MECH 6035
3 Semester Credits

Intelligent Systems Theory

This is a course for students in their first or second year of their graduate studies and for undergraduate students in the senior year. This course introduces and analyzes intelligent systems used in flexible manufacturing systems. The coursework includes expert systems, fuzzy logic, neural networks and applications with intelligent systems in manufacturing and material handling. The student's understanding gained from this course will be evaluated by a midterm and final exams, homework assignments and a course project.
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ENGR 6045
3 Semester Credits

Interdisciplinary Innovation for Engineers

Interdisciplinary Innovation for Engineers provides students in technical disciplines a practical and focused understanding of innovation competencies and processes needed to add high-order value to all organizations in a dynamic economy. This interdisciplinary course will enable students to develop foundational attitudes, skills, and knowledge for both core business and engineering innovation functions. This course is designed as a practical, systems-oriented, meaningful experience for learning future-oriented competencies to contribute to all types of organizations while accelerating career enhancement
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MECH 6078
3 Semester Credits

Introduction to Industrial Artificial Intelligence

Industrial big data includes all types of data generated from industry applications consisting of machine operation, manufacturing process and maintenance events, etc. In today’s competitive business environments, companies have urgent needs to use advanced analytical tools to manage their industrial data to gain more insights of their operations. This course will introduce students to advanced technologies—such as advanced signal processing, pattern recognition & machine learning and predictive analytics—that ultimately enable the conversion of industrial big data into actionable information that can be used to improve the design, the productivity and the efficiency of manufacturing operations.
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EECE 9060
1 Semester Credits

Master of Engineering Capstone Project

Student works under the direction of a faculty member to complete the MEng capstone project.
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ENGR 6040
3 Semester Credits

Management of Innovation

The Management of Innovation course is a comprehensive review of the concepts of imagination, creativity, innovation, and entrepreneurship. This practical course focuses on the twelve elements of innovation and the twelve innovation competencies: innovative behaviors, thinking, problem solving, knowledge, creativity insights, culture building, innovation, entrepreneurship, strategy, leadership, ecosystems, and technology accelerators. This online course is designed for student-centered learning and integrates personalization, challenge-based learning teams that develop a meaningful value-added prototype, and student produced evidence-based research mini-documentary videos.
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CS 6101
3 Semester Credits

Introduction to Applied Artificial Intelligence and Machine Learning Tools

This course is designed for professionals who already have basic knowledge of Python programming and basic algorithms, but are looking to refresh their knowledge and expand their hands-on skills on recent Machine Learning tools. Students will be introduced to popular AI techniques conceptually and will learn to evaluate the performance of the algorithms themselves, taking advantage of the latest tools available. Furthermore, they will learn how to adapt and customize the algorithms on new problems reviewing examples. Supervised learning techniques (regression, classification, neural networks and SVM), unsupervised learning techniques (clustering, SOM, PCA) and anomaly detection algorithms will be covered. This course is not approved for use in EECS Graduate programs.
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EECE 6023C
4 Semester Credits

Security & Trust for Cyberphysical Systems

Cyber-physical systems integrate computation, communication, and physical processes. They interact with the world in real time. With the growth of powerful networks and the Internet of Things (IoT), these systems are charged with performing many tasks for which reliability, safety, and security are critical. This course focuses on the development of trusted cyber-physical systems, which can be counted on to perform their tasks at defined levels of reliability, safety, and security.
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FAQs
  • Is Federal Financial Aid (Title IV) available?

    Yes. Many of our students qualify for some type of financial aid.

    Sources of aid:

  • Can I work while completing this program?

    Yes. The vast majority of our students work throughout their time in their academic program. It is important to assess course load and financial aid to understand how to balance school and working.

    If possible, students may cut down on their work hours during a clinical portion of a program.

  • Are there any onsite visits required for your programs?

    Most of our programs do not require onsite visits, but there are a couple of exceptions.

    If you are interested in pursuing the Master of Science in Nursing-Nurse Midwifery, you will have two skills intensives that take place on campus. The Post-Master’s Psychiatric-Mental Health Nurse Practitioner program has one onsite visit that occurs during the first semester of psych-specific classes.

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