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Electrical and Computer Engineering
 
 

 III. GENERAL ANNOUNCEMENTS (MEE)


Rice University publishes its “General Announcements” (GA) each year. These are the official rules of the university and include the honor code that every student agrees to abide by, as well as forms and research information. They can be found at ga.rice.edu. Two sections of this are of particular importance to graduate students in ECE. The first is the section titled “Graduate Degree Programs.” This outlines the basic rules and expectations for all graduate students at Rice University. The second, titled “Programs of Study,” is the department-specific information. This information covers the degree requirements for the M.E.E. and more information is found in Section V of this handbook. The ECE M.E.E. requirements from this section are reproduced below.

Graduate Degree Program
The Master of Electrical Engineering (MEE) degree is a course-based program designed to increase a student’s mastery of advanced subjects; no thesis is required. The MEE prepares a student to succeed and advance rapidly in today’s competitive technical marketplace. A coordinated MBA/MEE degree is offered in conjunction with the Jesse H. Jones Graduate School of Business. 

Information on admission to graduate programs is available from the Electrical and Compute Engineering Graduate Committee and on the Electrical and Computer Engineering website External Link. Students must achieve at least a B (3.0) average in the courses counted toward a graduate degree.

Program Learning Outcomes for the Master of Electrical Engineering Degree (MEE)

Upon completing the MEE degree, students will be able to:

Apply the principles of mathematics and science necessary to solve advanced electrical engineering problems.
Practice at an advanced level in at least one of the major sub-fields of electrical engineering.

Requirements for the Master of Electrical Engineering Degree (MEE)
For general university requirements, see Graduate Degrees. Students pursuing the MEE must complete:

A minimum of 10 courses (30 credit hours) at the 500-level or higher to satisfy degree requirements.
A minimum of 6 courses (18 credit hours) from an area of specialization.
A minimum of 2 courses (6 credit hours) from a minor area.
All courses applied toward the degree with a grade of 'C' or better.
No more than 1 course (3 credit hours) from transfer work.
ELEC 698 ECE Professional Masters Seminar Series each semester
Students are admitted to the MEE program in both fall and spring semesters. MEE students are to consult with an academic advisor on the MEE Committee each semester in order to identify and clearly document their individual curricular requirements or degree plan to be followed. An MEE degree planning form and current requirements may be found on the ECE website External Link.

AREAS OF SPECIALIZATION
Students must complete a minimum of 6 courses (18 credit hours) from one area of specialization and a minimum of 2 courses (6 credit hours) from another area of specialization as a minor area. The following courses represent typical courses under the current five areas of specialization that students may study as part of the MEE degree program. Students may take and are encouraged to take, with the approval of an academic advisor, other courses that are not listed below that are consistent with their career objectives. ELEC 590 may not count as major area courses.

Computer Engineering
ELEC 513/COMP 513 Complexity in Modern Systems [ 3 credit hours ]
ELEC 516 Analog Integrated Circuits [ 3 credit hours ]
ELEC 522 Advanced VLSI Design [ 3 credit hours ]
ELEC 524/COMP 524 Mobile and Wireless Networking [ 3 credit hours ]
ELEC 526/COMP 526 High Performance Computer Architecture [ 3 credit hours ]
ELEC 527 VLSI Systems Design [ 3 credit hours ]
ELEC 553 Mobile and Embedded Systems Design and Application [ 4 credit hours ]
ELEC 554/COMP 554 Computer Systems Architecture [ 4 credit hours ]

Data Science
ELEC 502/COMP 502/STAT 502 Neural Machine Learning I [ 3 credit hours ]
or COMP 540 Statistical Machine Learning [ 4 credit hours ]
ELEC 531 Statistical Signal Processing [ 3 credit hours ]
ELEC 533/CAAM 583/STAT 583 Introduction to Random Processes and Applications [ 3 credit hours ]
ELEC 535 Information Theory [ 3 credit hours ]
ELEC 557/COMP 557 Artificial Intelligence
ELEC 558 Digital Signal Processing [ 3 credit hours ]
ELEC 575 Learning from Sensor Data [ 3 credit hours ]
ELEC 631 Advanced Topics in Signal Processing [ 3 credit hours ]
STAT 640 Data Mining and Statistical Learning [ 3 credit hours ]
STAT 648 Graphical Models and Networks [ 3 credit hours ]

Neuroengineering
ELEC 502/COMP 502/STAT 502 Neural Machine Learning I [ 3 credit hours ]
ELEC 533/CAAM 583/STAT 583 Introduction to Random Processes and Applications [ 3 credit hours ]
ELEC 548/BIOE 548 Machine Learning and Signal Processing for Neuroengineering [ 3 credit hours ]
ELEC 580/BIOE 590 Introduction to Neuroengineering [ 3 credit hours ]
ELEC 585/BIOE 591 Fundamentals of Medical Imaging I [ 3 credit hours ]
ELEC 588 Theoret. Neuroscience: From Cells to Learning Systems [ 3 credit hours ]
ELEC 589 Theoret. Neuroscience: Networks and Learning [ 3 credit hours ]
ELEC 677 A Practical Intro. to Deep Machine Learning [ 3 credit
ELEC 680/BIOE 680 Nano-Neurotechnology [ 3 credit hours ]
STAT 640 Data Mining and Statistical Learning [ 3 credit hours ]

Photonics, Electronics, and Nano-Devices
ELEC 562 Optoelectronic Devices [ 3 credit hours ]
ELEC 568 Laser Spectroscopy [ 3 credit hours ]
ELEC 569/PHYS 569 Ultrafast Optical Phenomena [ 3 credit hours ]
ELEC 571 Imaging at the Nanoscale [ 3 credit hours ]
ELEC 603 Topics in Nanophotonics [ 2 credit hours ]
ELEC 605/PHYS 605 Electrodynamics & Nanophotonics [ 3 credit hours ]
ELEC 661/CHEM 661/MSNE 661 Nanophotonics and Sustainability [ 3 credit hours ]

Systems
ELEC 531 Statistical Signal Processing [ 3 credit hours ]
ELEC 533/CAAM 583/STAT 583 Introduction to Random Processes and Applications [ 3 credit hours ]
ELEC 535 Information Theory [ 3 credit hours ]
ELEC 542 Vector Spaces and DSP [ 3 credit hours ]
ELEC 547 Computer Vision [ 3 credit hours ]
ELEC 549 Computational Photography [ 3 credit hours ]
ELEC 551 Digital Communication [ 3 credit hours ]
ELEC 558 Digital Signal Processing [ 3 credit hours ]