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All students in the MS in Computer Engineering program must have previously completed (as documented by their undergraduate transcript), successfully test to place out of, or complete at the start of their program, the following courses: CSE 501N Programming Concepts and Practice and CSE 505N Introduction to Digital Logic and Computer Design.
The Master of Science in Computer Engineering degree is jointly administered by the Department of Computer Science & Engineering (CSE) and the Department of Electrical & Systems Engineering (ESE).
This option requires 30 units of graduate credit. Students must also follow the general degree requirements listed below.
Thesis/ Project Option
The thesis or project options require 24 units of graduate credit in addition to 6 units of either thesis or project courses (CSE/ESE 599 or CSE 598 respectively). Students pursuing the project option may opt to take 27 units of graduate courses and only 3 units of CSE 598 with adviser approval. Students must also follow the general degree requirements listed below.
General Degree Requirements
- 18 of the 30 units must be from the designated graduate-level Computer Engineering courses.
- Students may request approval to take up to 12 additional units from outside the CSE/ESE departments. Such approval shall be contingent on the credits being suitably technical graduate-level content. To count more than 6 units from outside the CSE/ESE departments, an appropriate justification for the additional increment shall be provided by the adviser and student. Departmental approval shall be evaluated with increasing stringency for each additional increment.
- Up to 12 units of 400-level courses can count for graduate credit.
- None of the 30 units may be taken as independent study (i.e., CSE 400, CSE 500, ESE 400 or ESE 500).
- Courses with an "N" designation do not count toward the master's degree.
- All courses must be taken for a grade of C- or better.
- As per Engineering School guidelines, students must maintain a GPA of at least 2.70.
|Advanced Real-Time Embedded Systems
|Probability and Stochastic Processes
|Wireless Embedded Sensor Networks
|Advanced Operating Systems
|Natural Language Processing
|Advanced Multiparadigm Software Development
|Modeling and Performance Evaluation of Interconnected Computer Systems
|Concepts in Multicore Computing
|Control Systems Design by State Space Methods
|Robust and Adaptive Control
|Theory of Parallel Systems
|Computer Systems Architecture I
|Computer Systems Architecture II
|Digital Integrated Circuit Design and Architecture
|Acceleration of Algorithms in Reconfigurable Logic
|High Performance Computer Systems
|Modern System-on-Chip Design
|Computer Systems Analysis
|Parallel Architectures & Algorithms
|Recent Advances in Networking
|Wireless and Mobile Networking
|Bayesian Methods in Machine Learning
|Advanced Machine Learning
|CSE 534A/ESE 513
|Large-Scale Optimization for Data Science
|Advanced IoT, Real-Time, and Embedded Systems Security
|Detection and Estimation Theory
|Linear Dynamic Systems I
|Nonlinear Dynamic Systems
|Analog Integrated Circuits
|Signaling and Control in Communication Networks