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Online Master’s Engineering Programs

Online M.S. in Electrical and Computer Engineering Curriculum

The online Master of Science in Electrical and Computer Engineering offers two plans of study, a thesis and non-thesis option. The curriculum is built to empower electrical and computer engineers to follow their passions, confront technical challenges and develop powerful new devices. Our courses in high-demand fields like autonomous vehicles, neural networks and diamond technology give professionals the advanced knowledge of systems and platforms that they need to usher in the next wave of revolutionary advances.

The pioneering researchers of MSU’s faculty shaped the electrical and computer engineering curriculum and regularly update courses with the concepts and technologies that graduates need to excel in cutting-edge organizations. You’ll have opportunities to receive individualized mentorship and pursue your own areas of interest as you complete a program that’s built for a constantly evolving world of fast-paced innovation.

Below are the requirements for completion for each study plan. Students in both the thesis and non-thesis option must take at least two courses (six credits) from the following selection of courses:

  • ECE 813: Advanced VLSI Design
  • ECE 820: Advanced Computer Architecture
  • ECE 821: Advanced Power Electronics and Applications
  • ECE 835: Advanced Electromagnetic Fields and Waves I
  • ECE 851: Linear Control Systems
  • ECE 863: Analysis of Stochastic Systems
  • ECE 874: Physical Electronics

Master’s degree Plan A (thesis option) Requirements


  • Total credit requirement: 30 credits (courses below 400 level may not be counted)
    • 12 Credits in ECE courses at the 800- or 900-level
    • 6 credits in supporting classes from outside the MSU College of Engineering. Note that more courses may be offered online in the future. The current online courses that fulfill the breadth requirement include:
      • STT 861, 875
    • A minimum of 4 credits and no more than 8 credits of ECE 899 (thesis research)
    • A minimum of 20 credits at the 800 level or above (including thesis credits)
    • First-year graduate students must attend a minimum of 7 seminars from the graduate seminar series. All live sessions are recorded and are made available for students to watch later if they are unable to attend the live sessions.
  • Students who elect the thesis option will also need to work with an ECE faculty member to advise them with their thesis research.

Master’s degree Plan B (non-thesis option) Requirements


  • Total credit requirement: 30 credits (courses below 400 level may not be counted)
    • 12 Credits in ECE courses at the 800- or 900-level
    • 6 credits in supporting classes from outside the MSU College of Engineering. Note that more courses may be offered online in the future. The current online courses that fulfill the breadth requirement include:
      • STT 861, 875
    • A minimum of 18 credits at the 800 level or above.
    • First-year graduate students must attend a minimum of 7 seminars from the graduate seminar series. All live sessions are recorded and are made available for students to watch later if they are unable to attend the live sessions.

Graduate Electrical and Computer Engineering Course Descriptions


Note: Not all courses are available every semester. Contact an Admission Counselor for more information on course availability and to discuss your desired academic plan.

The Autonomous Vehicles (AV) class provides both an introduction to AVs and a hands-on programming of real and virtual AVs. Technologies addressed include: sensors, computer vision, artificial intelligence (AI), planning, communication, security and software architectures. Broader impacts of AVs are reviewed including social and legal impacts, along with risks and ethical problems. The hands-on portion teaches the Robot Operating System (ROS) and how to use it to control AVs: from reading sensor data, detecting objects with computer vision and AI, to circumnavigating obstacles. For their final project, students will each program an AV to traverse a real or virtual world. Proficiency in Python is assumed.
An exploration of automated ground vehicles and enabling technologies. Research focused, with critical reading, discussion, and presentation assignments. Teams conduct hands-on research using ROS and simulators or hardware platforms (on-campus only) to prepare a submission-ready conference or journal manuscript. Topics include: AV sensing, connectivity, and control technologies; machine and deep learning; use cases and human factors; tracking, prediction, and routing; localization and navigation; security, reliability, and deployment challenges. The difference between intro and advanced topics is primarily the emphasis on research – in ATAV’s, we read recent conference and journal papers and students conduct a literature review as they write their own papers.
Prerequisite: ECE 410
Advanced topics in digital integrated circuit design. Design specifications: functionality, performance, reliability, manufacturability, testability, cost. Standard cells. Design-rule checking. Circuit extraction, simulation, verification. Team-based design.
Robot modeling, kinematics, dynamics, trajectory planning, programming, sensors, controller design.
Operation planning of power systems including load flow, unit commitment, production cost methods. On line operation and control including automatic generation control, economic dispatch, security assessment, state estimation.
This course focuses on distributed-analysis and design of microwave and millimeter wave circuits and systems. Topics include review of Maxwell’s equations and application of EM for component and circuit design, network analysis, review of high frequency semiconductor devices, waveguides and transmission lines, wave propagation, couplers, filters, phase shifters, isolators, detectors and mixers, amplifiers, oscillators, device and system noise analysis, systems packaging, calibration and S-parameter measurements.
Diamond has a host of outstanding properties including highest thermal conductivity, extreme hardness, wide bandgap, large electric field breakdown strength, high hole and electron mobility, high radiation hardness, good electrochemical performance, and good chemical inertness. Diamond has applications and potential applications in electronics, optics, sensors, MEMS, wear/cutting, quantum computing, and thermal management. This course is intended to provide the student with a state-of-the-art knowledge of diamond properties, diamond devices/applications, and diamond growth/processing/manufacturing technology.
Electrostatics, magnetostatics, electrodynamics and Maxwell’s equations. Potential functions. Eigenfunction expansion. Green’s functions. Radiation of EM waves. EM boundary-value problems. TEM waves. Maxwell’s equations with magnetic sources.

Definitions for Pareto-optimality and systematic computerized algorithms for finding Pareto-optimal solutions. Classical generative and emerging methods using evolutionary optimization methods. Decision-making tasks to choose a final preferred solution.
Vector spaces, representation, system description, solution to the state equations, stability, controllability and observability. Adjoints of linear maps. Eigenstructure assignment. Partial and full order observers. Disturbance decoupling.
This course provides an introduction to the current state-of-the-art of deep neural networks with emphasis on implementations and project execution. The buzz word now is “deep learning.” The course will identify the elements of “deep learning”. What makes a basic neural network become a deep learning network. The popular architectures are (i) feedforward: deep neural networks (DNN), convolutional neural network (ConvNets), and (ii) Feedback: (simple) recurrent neural networks (RNN), Long Short Term Memory (LSTM) RNNs, Gated RNNs, etc., and also (iii) a combination of the feedforward and feedback architectures. The main learning used is frequently a form of the Stochastic Gradient Descent (SGD) and its variations. The course will focus on the advantages and limitations of several neural models and architectures. Common and new applications of neural networks will also be highlighted during the class.
Applications of quantum mechanics and statistical mechanics in solids. Band theory of semiconductors. Electrical transport phenomena. Pn junctions.

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