Master of Science in Computer Engineering
The Master of Science in Computer Engineering educates students to acquire a strong fundamental background in computer engineering and state-of-the-art knowledge and hardware and software skills applied for cutting edge areas such as embedded systems, networks and security, software and data engineering, and artificial intelligence. The program focus is on problem-solving skills development for real-world applications, with an emphasis on research experience. Our student-centric learning environment provides a variety of opportunities, accelerated graduate degrees, co-op opportunities and internships.
In general, a bachelor’s degree in electrical engineering or computer engineering with a minimum grade point average of on a 4.0 scale is required for graduate study in computer engineering. Outstanding applicants in other areas may be conditionally admitted subject to the completion of appropriate ramp courses or their equivalents with a grade of “B” or better. The specific requirements will be determined on an individual basis depending upon the student’s background. Submission of GRE scores is recommended, but not required.
The master’s degree requires completion of a total of 30 hours of credit. Each student must complete a mathematical foundations course, four core courses and must complete the course requirements for one of the computer engineering concentrations. Elective courses are to be chosen from the CPE, EE or AAI numbered graduate courses in this catalog. An elective course not in the CPE, EE or AAI numbered courses may be taken, with the approval of the student’s academic advisor. A maximum of two elective courses not listed in the ECE program may be taken with the approval of the academic advisor.
Program Outcomes
By the time of graduation, students will be able to
- apply knowledge of mathematics and physics to problem solving in computer engineering
- analyze computer systems using engineering principles and modeling approaches
- design experiments and analyze results to determine process parameters, and to identify issues and methods for computer engineering measurements
- use mathematical, modeling, and engineering principles to design computer engineering processes; be able to incorporate considerations such as feasibility, applicability, cost, legal/regulatory, societal impacts, etc. in designs
- use computer software for data analysis, reporting and presentations
- be capable of writing and presenting polished technical reports at a level expected of the engineering profession, and be able to critically evaluate the technical literature and use it to obtain solutions to computer engineering problems
- work effectively in a team
- develop intellectual property
- recognize and achieve high levels of professionalism in work and understand ethical and moral systems in a social contex
Master of Science in Computer Engineering Requirements
The Master of Science in Computer Engineering is a 30-credit degree program. Students seeking a Master of Science (MS) in Computer Engineering are required to complete:
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One (1) mathematical foundation course
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Four (4) core courses in their majors/programs
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Three (3) concentration courses in a chosen concentration
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Three (3) credits of project work (CPE800) co-requisite with EE820 (0 credit) and Three (3) credits elective course, or Six (6) credits thesis
Mathematical Foundation Courses
Students are required to select one mathematical foundation course from the list below:
| CPE 602 | Applied Discrete Mathematics | 3 |
| Or | |
| EE 605 | Probability and Stochastic Processes I | 3 |
Core Courses
Students are required to select four courses from the following list:
| CPE 517 | Digital and Computer Systems Architecture | 3 |
| CPE 555 | Real-Time and Embedded Systems | 3 |
| CPE 593 | Applied Data Structures and Algorithms | 3 |
| CPE 690 | Introduction to VLSI Design | 3 |
| EE 608 | Applied Modeling and Optimization | 3 |
Concentrations
Students are required to select three courses from one of the concentrations listed below:
Artificial Intelligence Concentration Course Options
Students who select the Artificial Intelligence concentration are required to select three concentration courses from the list below:
| AAI 551 | Engineering Programming: Python | 3 |
| AAI 672 | Applied Game Theory and Evolutionary Algorithms | 3 |
| AAI 627 | Data Acquisition, Modeling and Analysis: Big Data Analytics | 3 |
| AAI 628 | Data Acquisition, Modeling and Analysis: Deep Learning | 3 |
| AAI 646 | Pattern Recognition and Classification | 3 |
| AAI 695 | Applied Machine Learning | 3 |
Embedded Systems Concentration Course Options
Students who select the Embedded Systems concentration are required to select three concentration courses from the list below:
| CPE 517 | Digital and Computer Systems Architecture | 3 |
| CPE 555 | Real-Time and Embedded Systems | 3 |
| CPE 556 | Computing Principles for Mobile and Embedded Systems | 3 |
| CPE 690 | Introduction to VLSI Design | 3 |
| EE 629 | Internet of Things | 3 |
Networks and Security Concentration Course Options
Students who select the Network and Systems Security concentration are required to select three concentration courses from the list below:
| CPE 579 | Foundations of Cryptography | 3 |
| Or | |
| CS 579 | Foundations of Cryptography | 3 |
| | |
| EE 584 | Wireless Systems Security | 3 |
| CPE 654 | Design and Analysis of Intelligent Network Systems | 3 |
| CPE 679 | Computer and Information Networks | 3 |
| CPE 691 | Information Systems Security | 3 |
Software and Data Engineering Concentration Course Options
Students who select the Software and Data Engineering concentration are required to select three concentration courses from the list below:
| CPE 593 | Applied Data Structures and Algorithms | 3 |
| EE 551 | Engineering Programming: Python | 3 |
| EE 552 | Engineering Programming: Java | 3 |
| EE 553 | Engineering Programming: C++ | 3 |
| | |
| EE 627 | Data Acquisition, Modeling and Analysis: Big Data Analytics | 3 |
| Or | |
| AAI 627 | Data Acquisition, Modeling and Analysis: Big Data Analytics | 3 |
| | |
| | |
| EE 628 | Data Acquisition, Modeling and Analysis: Deep Learning | 3 |
| Or | |
| AAI 628 | Data Acquisition, Modeling and Analysis: Deep Learning | 3 |
| | |
| EE 629 | Internet of Things | 3 |
| CPE 810 | Special Topics in Computer Engineering | 3-6 |
Project or Thesis
Students in the Master of Science program are required to complete:
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Project Track: 3 credit project course (800 course) and a 3 credit elective course at the 500 or 600 level. Students who enroll in the 3 credit project course (800 course) are required to enroll in the 0-credit co-requisite research seminar course, EE 820. The 3 credit elective course can be any graduate level course at the 500 or 600 levels within the Department of Electrical and Computer Engineering. Elective courses that are taken outside of the department require approval by the faculty advisor.
OR