Computer Engineering – MS

Master of Science in Computer Engineering

An MS in Computer Engineering offers advanced knowledge and skills in hardware and software systems, enabling students to take on challenging roles in some of the emergent areas of artificial intelligence, VLSI Systems, Embedded Systems, and more. The program combines rigorous classwork with hands-on projects to develop expertise in cutting-edge technologies and innovative problem-solving. The graduates are thereby well-equipped to handle the most complex engineering problems.

Three core courses define the Computer Engineering degree program. Students must complete the remaining courses from department and school-wide electives. To satisfy their degree requirements, students must achieve an overall GPA (grade point average) of 3.0 or better, a GPA of 3.0 or better in their core MSCE classes, and a grade of B—or better in all their core MS CE classes. One 5000-level computer engineering course can be counted towards the graduate semester credit hours.

The MSCE degree offers three tracks: Applied Machine Learning, Embedded and VLSI Systems, and Open Track. Applied Machine Learning and Embedded and VLSI Systems are considered “focused” tracks.

For all tracks, each student must complete a total of 30 hours from three categories:

I. CE-Depth (Core) Courses: 9 semester credit hours (all tracks)

Each student must complete the following three ( 3) CE-Depth courses, making a B- or better in each course and an overall GPA of 3.0 or better in the Core courses:

• CE 6304 Computer Architecture
• CE 6302 Embedded Systems
• CE 6320 Applied Data Structures and Algorithms

II. CE-Breadth Electives Courses: 15 semester credit hours (all tracks)

Each student must complete five (5) CE-Breadth Electives courses. Electives must be 6000 level or above courses. At most, nine (9) credit hours can be CS courses in this group. One pre-approved independent study is allowed.

Thesis Option: This option requires at least one three-credit hour thesis and three to six hours of research. An independent study would count towards research.

III. ECS Free Electives: 6 semester credit hours

Each student must complete two (2) ECS Free Electives courses that satisfy the following:

• Must be a course in ECS School.
• Only one 5000 level course is allowed.
• Pre-approval is required for: (a) one independent study (by a faculty advisor), and (b) up to two non-ECS courses (by MS Thesis advisor).

CE Focused Tracks

The MSCE degree also offers two focused tracks. Students have a choice of choosing one of the two focused tracks. Students wanting to have the track indicated on their transcripts must take at least three courses under the two tracks.

Applied Machine Learning is a track that introduces the student to the basic concepts and techniques of machine learning. Some of the topics addressed in this coursework include data preprocessing, supervised and unsupervised learning, model evaluation, and feature engineering. Working on real-world projects and assignments gives students hands-on experience using popular machine-learning algorithms, tools, and libraries. Students will be empowered to build, evaluate, and deploy machine learning models in various application domains.

Students must complete at least three out of the following courses.

• CS 6375 Machine Learning
• CS 6347 Statistical Methods in AI and Machine Learning
• EESC 6364 Machine Learning and Pattern Recognition
• CE 6309 Applications of Machine Learning in Semiconductor IC Manufacturing and Test

Embedded and VLSI Systems track addresses the detailed design, implementation, and optimization of embedded systems and Very-Large-Scale-Integration (VLSI) circuits. It involves coursework on digital logic design concepts, microcontroller programming, Hardware Description Languages, and integrated circuits design methodologies. Hands-on experience with projects and practical assignments provides proficiency in designing and verifying embedded systems and VLSI circuits, which the students can carry into professional endeavors in electronics and hardware engineering.

Students must complete at least three out of the following courses.

• CE 6370 Design and Analysis of Reconfigurable Systems
• EESC 6367 Applied Digital Signal Processing
• CE 6325 VLSI Design
• CE 6303 Testing and Testable Design
• CE 6375 Design Automation of VLSI Systems