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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 with the ability to follow their passions while offering a sophisticated electrical engineering education.

With this philosophy in mind, the master’s in electrical and computer engineering program provides exposure to the concepts and technologies graduates will need to be successful.

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.

A hands-on introduction to autonomous vehicles (AVs) with topics including applications for road-going AVs, perception, connectivity, neural networks, and challenges to AV deployment including cybersecurity and human factors. Students will gain familiarity with Robotic Operating System (ROS), sensors (IMUs, LIDAR, cameras, RADAR, and SONAR), perception (localization, mapping, object recognition and tracking, and sensor fusion), simulation, and decision making (path planning, prediction, obstacle avoidance). Participants will complete individual assignments to build ROS skills and work in teams of three or four to develop or evaluate an AV technology. Course deliverables include a submission-ready conference paper, two team presentations (one identifying problems /opportunities, and one presenting and evaluating your own solution or surveying the state-of-the-art in solving that problem), and a live demonstration.
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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