Master of Electrical and Computer Engineering (MECE) Degree
Program Learning Outcomes for the MECE Degree
Upon completing the MECE degree, students will be able to:
- Design and implement technical solutions to real-world problems that reflect an advanced command of principles in mathematics and science.
- Communicate effectively expert analysis of technical problems and features of proposed solutions to stakeholders.
- Practice as an expert specialist in at least one of the major sub-fields of electrical and computer engineering.
Requirements for the MECE Degree
The MECE degree is a non-thesis master's degree. For general university requirements, please see Non-Thesis Master's Degrees. For additional requirements, regulations, and procedures for all graduate programs, please see All Graduate Students. Students pursuing the MECE degree must complete:
- A minimum of 10 courses (30-34 credit hours, depending on course selection) to satisfy degree requirements.
- A minimum of 30 credit hours of graduate-level study (graduate semester credit hours, coursework at the 500-level or above).
- A minimum of 27 graduate semester credit hours must be taken at Rice University.
- A minimum of 24 graduate semester credit hours must be taken in standard or traditional courses (with a course type of lecture, seminar, laboratory, lecture/laboratory).
- A minimum residency enrollment of one fall or spring semester of part-time graduate study at Rice University.
- A minimum of 3 courses (9 credit hours) from the Capstone Requirement.
- 1 course (3 credit hours) to fulfill the Capstone Foundations requirement.
- 2 courses (6 credit hours) to fulfill the Capstone Experience Project requirement.
- A minimum of 1 course (3 credit hours) from the Engineering Communications Requirement.
- A minimum of 2 courses (6 credit hours) from the Engineering Software Development Requirement.
- A minimum of 2 courses (6 credit hours) in one area of specialization (see below for areas of specialization). The MECE degree program offers seven areas of specialization:
- Computer Engineering, or
- Computer Vision, or
- Data Science, or
- Digital Health, or
- Neuroengineering, or
- Quantum Engineering, or
- Wireless Systems.
- A minimum of 2 courses (6 credit hours) from the Elective Requirements.
- ELEC 698 each semester in residence at Rice University.
- A maximum of 1 course (3 graduate semester credit hours) from transfer credit. For additional departmental guidelines regarding transfer credit, see the Policies tab.
- A minimum overall GPA of 2.67 or higher in all Rice coursework.
- A minimum program GPA of 3.00 or higher in all Rice coursework that satisfies requirements for the non-thesis master’s degree with a minimum grade of C (2.00 grade points) in each course.
Students are admitted to the MECE degree program in the fall semester. MECE students are to consult with an academic advisor on the MECE Committee each semester in order to identify and clearly document their individual curricular requirements or degree plan to be followed.
The courses listed below satisfy the requirements for this degree program. In certain instances, courses not on this official list may be substituted upon approval of the program's academic advisor or, where applicable, the department or program's Director of Graduate Studies. Course substitutions must be formally applied and entered into Degree Works by the department or program's Official Certifier. Additionally, these course substitutions must be approved by the Office of Graduate and Postdoctoral Studies. Students and their academic advisors should identify and clearly document the courses to be taken.
Summary
Code | Title | Credit Hours |
---|---|---|
Total Credit Hours Required for the MECE Degree | 30-34 |
Degree Requirements
Code | Title | Credit Hours |
---|---|---|
Capstone Requirement | ||
Select 1 of the following Capstone topical areas: Computer Engineering, Computer Vision, Data Science, Digital Health, Neuroengineering, Quantum Engineering, or Wireless Systems | ||
Capstone: Foundations | ||
Select 1 course from the following: | 3 | |
ADVANCED VLSI DESIGN | ||
MODERN COMMUNICATION THEORY AND PRACTICE | ||
A PRACTICAL INTRODUCTION TO DEEP MACHINE LEARNING | ||
INTRODUCTION TO MACHINE LEARNING | ||
QUANTUM MECHANICS AND REAL-WORLD APPLICATIONS | ||
INTRODUCTION TO NEUROENGINEERING: MEASURING AND MANIPULATING NEURAL ACTIVITY | ||
Capstone: Experience Project | ||
Select 1 from the following (minimum of 2 semesters): | 6-8 | |
APPLIED MACHINE LEARNING AND DATA SCIENCE PROJECTS (2 semesters required) | ||
MECE CAPSTONE PROJECT (2 semesters required) | ||
Engineering Communications Requirement | ||
Select 1 course from the following: | 3 | |
WORKPLACE COMMUNICATION FOR PROFESSIONAL MASTER'S STUDENTS IN ENGINEERING | ||
TECHNICAL AND MANAGERIAL COMMUNICATIONS | ||
ENGINEERING PERSUASION: HOW TO DRIVE DECISIONS AND CHANGE | ||
PROFESSIONAL COMMUNICATION FOR ENGINEERING LEADERS | ||
Engineering Software Development Requirement | ||
Select 2 courses from the following: | 6-8 | |
GRADUATE OBJECT-ORIENTED PROGRAMMING AND DESIGN | ||
INTRODUCTION TO DATABASE SYSTEMS | ||
PARALLEL COMPUTING | ||
SOFTWARE ENGINEERING METHODOLOGY | ||
BIG DATA MANAGEMENT FOR DATA SCIENCE | ||
FUNDAMENTALS OF ROBOTIC MANIPULATION | ||
COMPUTER PROGRAMMING FOR DATA SCIENCE | ||
GRADUATE DESIGN AND ANALYSIS OF ALGORITHMS | ||
INTRODUCTION TO COMPUTER VISION | ||
ALGORITHMIC ROBOTICS | ||
OPERATING SYSTEMS AND CONCURRENT PROGRAMMING | ||
SOLID STATE MATERIALS AND DEVICE APPLICATIONS | ||
INTRODUCTION TO QUANTUM COMPUTING WITH QISKIT | ||
QUANTUM INFORMATION PROCESSING SYSTEMS | ||
QUANTUM PHYSICS IN SEMICONDUCTOR DEVICES | ||
R FOR DATA SCIENCE | ||
SAS STATISTICAL PROGRAMMING | ||
Area of Specialization | ||
Select 1 of the following Areas of Specialization (see Areas of Specialization below): | 6 | |
Computer Engineering | ||
Computer Vision | ||
Data Science | ||
Digital Health | ||
Neuroengineering | ||
Quantum Engineering | ||
Wireless Systems | ||
Elective Requirements | ||
Free Elective Requirement: select 2 additional courses as free electives 1 | 6 | |
Professional Master's Seminar | ||
ELEC 698 | ECE PROFESSIONAL MASTERS SEMINAR SERIES 2 | 0 |
Total Credit Hours | 30-34 |
Footnotes and Additional Information
1 | The Free Elective Requirement may be fulfilled by any 2 courses (6 credit hours) selected from the following:
|
2 | ELEC 698 is taken for a Satisfactory/Unsatisfactory grade and must be completed with a Satisfactory grade. As a S/U course it does not apply to the requirement of a minimum grade of C (2.00 grade points) in each required course. |
Areas of Specialization
Students must complete a minimum of 2 courses (6 credit hours) from one Area of Specialization.
Area of Specialization: Computer Engineering
Code | Title | Credit Hours |
---|---|---|
Select 2 courses (6 credit hours) from the following: | 6 | |
MACHINE LEARNING FOR RESOURCE-CONSTRAINED PLATFORMS | ||
ANALOG INTEGRATED CIRCUITS | ||
MICROWAVE ENGINEERING | ||
ADVANCED DIGITAL INTEGRATED CIRCUITS DESIGN | ||
ADVANCED VLSI DESIGN | ||
INTRODUCTION TO MICROFABRICATION | ||
HIGH PERFORMANCE COMPUTER ARCHITECTURE | ||
VLSI SYSTEMS DESIGN | ||
ADVANCED HIGH-SPEED SYSTEM DESIGN | ||
MOBILE AND EMBEDDED SYSTEM DESIGN AND APPLICATION | ||
COMPUTER SYSTEMS ARCHITECTURE | ||
UBIQUITOUS AND WEARABLE COMPUTING | ||
Total Credit Hours | 6 |
Area of Specialization: Computer Vision
Code | Title | Credit Hours |
---|---|---|
Select 2 courses (6 credit hours) from the following: | 6 | |
NEURAL MACHINE LEARNING I | ||
or COMP 540 | STATISTICAL MACHINE LEARNING | |
MACHINE LEARNING FOR RESOURCE-CONSTRAINED PLATFORMS | ||
STATISTICAL SIGNAL PROCESSING | ||
INTRODUCTION TO RANDOM PROCESSES AND APPLICATIONS | ||
3D VISION: FROM AUTONOMOUS CARS TO THE METAVERSE | ||
GENERATIVE AI FOR IMAGE DATA | ||
INTRODUCTION TO COMPUTER VISION | ||
COMPUTATIONAL PHOTOGRAPHY | ||
MOBILE AND EMBEDDED SYSTEM DESIGN AND APPLICATION | ||
DIGITAL SIGNAL PROCESSING | ||
LEARNING FROM SENSOR DATA | ||
A PRACTICAL INTRODUCTION TO DEEP MACHINE LEARNING | ||
INTRODUCTION TO MACHINE LEARNING | ||
ADVANCED MACHINE LEARNING |
Area of Specialization: Data Science
Code | Title | Credit Hours |
---|---|---|
Select 2 courses (6 credit hours) from the following: | 6 | |
NEURAL MACHINE LEARNING I | ||
or COMP 540 | STATISTICAL MACHINE LEARNING | |
LINEAR ALGEBRA FOR DATA SCIENCE | ||
MACHINE LEARNING FOR RESOURCE-CONSTRAINED PLATFORMS | ||
DATA SCIENCE AND DYNAMICAL SYSTEMS | ||
STATISTICAL SIGNAL PROCESSING | ||
INTRODUCTION TO RANDOM PROCESSES AND APPLICATIONS | ||
INFORMATION THEORY | ||
INTRODUCTION TO COMPUTER VISION | ||
DIGITAL SIGNAL PROCESSING | ||
LEARNING FROM SENSOR DATA | ||
A PRACTICAL INTRODUCTION TO DEEP MACHINE LEARNING | ||
INTRODUCTION TO MACHINE LEARNING | ||
ADVANCED MACHINE LEARNING | ||
Total Credit Hours | 6 |
Area of Specialization: Digital Health
Code | Title | Credit Hours |
---|---|---|
Select 2 courses (6 credit hours) from the following: | 6 | |
INTRODUCTION TO RANDOM PROCESSES AND APPLICATIONS | ||
3D VISION: FROM AUTONOMOUS CARS TO THE METAVERSE | ||
GENERATIVE AI FOR IMAGE DATA | ||
INTRODUCTION TO DIGITAL IMAGE AND VIDEO PROCESSING | ||
INTRODUCTION TO COMPUTER VISION | ||
DIGITAL SIGNAL PROCESSING | ||
DISTRIBUTED METHODS FOR OPTIMIZATION AND MACHINE LEARNING | ||
Total Credit Hours | 6 |
Area of Specialization: Neuroengineering
Code | Title | Credit Hours |
---|---|---|
Select 2 courses (6 credit hours) from the following: | 6 | |
NEURAL MACHINE LEARNING I | ||
INTRODUCTION TO MICROFABRICATION | ||
INTRODUCTION TO RANDOM PROCESSES AND APPLICATIONS | ||
MACHINE LEARNING AND SIGNAL PROCESSING FOR NEURO ENGINEERING | ||
IMAGING OPTICS | ||
INTRODUCTION TO NEUROENGINEERING: MEASURING AND MANIPULATING NEURAL ACTIVITY | ||
THEORETICAL NEUROSCIENCE I: BIOPHYSICAL MODELING OF CELLS AND CIRCUITS | ||
NEURAL COMPUTATION | ||
NANO-NEUROTECHNOLOGY | ||
SPOTLIGHT ON LATEST NEUROTECHNOLOGY | ||
Total Credit Hours | 6 |
Area of Specialization: Quantum Engineering
Code | Title | Credit Hours |
---|---|---|
Select 2 courses (6 credit hours) from the following: | 6 | |
MICROWAVE ENGINEERING | ||
INTRODUCTION TO MICROFABRICATION | ||
PHYSICS OF SENSOR MATERIALS AND NANOSENSOR TECHNOLOGY | ||
OPTOELECTRONIC DEVICES | ||
INTRODUCTION TO SOLID STATE PHYSICS I | ||
NANOPHOTONICS AND METAMATERIALS | ||
INTRODUCTION TO QUANTUM COMPUTING WITH QISKIT | ||
ULTRAFAST OPTICAL PHENOMENA | ||
IMAGING AT THE NANOSCALE | ||
FINITE ELEMENT METHOD FOR MULTIPHYSICS MODELING | ||
QUANTUM MECHANICS AND REAL-WORLD APPLICATIONS | ||
COMPUTATIONAL ELECTRODYNAMICS AND NANOPHOTONICS | ||
QUANTUM INFORMATION SCIENCE AND TECHNOLOGY | ||
Total Credit Hours | 6 |
Area of Specialization: Wireless Systems
Code | Title | Credit Hours |
---|---|---|
Select 2 courses (6 credit hours) from the following: | 6 | |
STATISTICAL SIGNAL PROCESSING | ||
INTRODUCTION TO RANDOM PROCESSES AND APPLICATIONS | ||
INFORMATION THEORY | ||
INTRODUCTION TO COMMUNICATION NETWORKS | ||
MODERN COMMUNICATION THEORY AND PRACTICE | ||
DIGITAL SIGNAL PROCESSING | ||
NETWORK SCIENCE AND ANALYTICS | ||
Total Credit Hours | 6 |
Policies for the MECE Degree
Department of Electrical and Computer Engineering Graduate Program Handbook
The General Announcements (GA) is the official Rice curriculum. As an additional resource for students, the department of Electrical and Computer Engineering publishes a graduate program handbook, which can be found here: https://gradhandbooks.rice.edu/2024_25/Electrical_Computer_Engineering_Graduate_Handbook.pdf.
Transfer Credit
For Rice University’s policy regarding transfer credit, see Transfer Credit. Some departments and programs have additional restrictions on transfer credit. Requests for transfer credit must be approved for Rice equivalency by the appropriate academic department offering the Rice equivalent course (corresponding to the subject code of the course content) and by the Office of Graduate and Postdoctoral Studies (GPS). Students are encouraged to meet with their academic program’s advisor when considering transfer credit possibilities.
Departmental Transfer Credit Guidelines
Students pursuing the MECE degree should be aware of the following departmental transfer credit guideline:
- No more than 1 course (3 credit hours) of transfer credit from U.S. or international universities of similar standing as Rice may apply towards the degree.
Teaching Assistant Requirement
Students must be enrolled in at least 5 credit hours to be able to serve as a teaching assistant (TA).
Additional Information
For additional information, please see the Electrical and Computer Engineering website: https://www.ece.rice.edu/.
Opportunities for the MECE Degree
Fifth-Year Master's Degree Option for Rice Undergraduate Students
In certain situations and with some terminal master's degree programs, Rice students have an option to pursue a master’s degree by adding an additional fifth year to their four years of undergraduate studies.
Advanced Rice undergraduate students in good academic standing typically apply to the master’s degree program during their junior or senior year. Upon acceptance, depending on course load, financial aid status, and other variables, they may then start taking some required courses of the master's degree program. A plan of study will need to be approved by the student's undergraduate major advisor and the master’s degree program director.
As part of this option and opportunity, Rice undergraduate students:
- must complete the requirements for a bachelor's degree and the master's degree independently of each other (i.e. no course may be counted toward the fulfillment of both degrees).
- should be aware there could be financial aid implications if the conversion of undergraduate coursework to that of graduate level reduces their earned undergraduate credit for any semester below that of full-time status (12 credit hours).
- more information on this Undergraduate - Graduate Concurrent Enrollment opportunity, including specific information on the registration process can be found here.
Rice undergraduate students completing studies in science and engineering may have the option to pursue the Master of Electrical and Computer Engineering (MECE) degree. For additional information, students should contact their undergraduate major advisor and the MECE program director.
Additional Information
For additional information, please see the Electrical and Computer Engineering website: https://www.ece.rice.edu/.