Electrical & Computer Engineering Department

Master of Science in Computer Engineering

As technology advances ever more rapidly, the computer industry and society as a whole need professionals who possess a combination of electronic hardware and computer software skills. These skills should be developed in the context of modern systems to make them more practical and useful. 

Artificial Intelligence (AI) innovations and applications, the Internet of Things (IoT), Networking and 5G wireless communications, and embedded systems are changing many aspects of daily life, including driving, entertainment, communication, health care, and virtual and robotic assistants. These changes are creating many new engineering jobs in the fields of AI applications, AI chip design, smart edge-device design, IoT system design, and intelligent system design.

The Master of Science in Computer Engineering (MSCE) degree program will focus on the overlap of electrical engineering and computer science. MSCE students will learn to design embedded systems that power the IoT system and study the design techniques that are used in wearable vital sign monitoring devices such as Apple watch. The MSCE students can take the AI concentration as an option. The AI concentration is designed based on the latest trends in the industry in developing Deep Learning and reinforcement learning algorithms and deploying them on the edge devices.


Our 39 credit hour curriculum is completed in 16 months. The 39 credit hours are composed of core courses, electives, cross disciplinary electives, capstone or thesis, and an internship.

Program Structure

Required Courses

  • 3 Core Courses (9 credit hours)
  • Elective Courses (15 credit hours)
  • Capstone course – Project or Thesis (3 credit hours)
  • Internship (up to 9 credit hour)
  • Nugget courses (up to 3 credit hours)
    • ITU Presents (1 credit hour)
    • ITU Nuggets (2 credit hours)

Core Courses

CEN 548 Computer Network Systems
ECE 577 Artificial Intelligence and Machine Learning Applications
EEN 541 Digital Signal Processing and System Analysis

Elective Courses: 15 Credit Hours

  • Field Relevant Courses in the ECE Department.
  • Internship: up to 9 credit hours
  • With approval from the ECE Department any course from
    • Department of Business Administration
    • Department of Computer Science
    • Department of Digital Arts
    • Department of Electrical and Computer Engineering
  • Transfer Credits: A maximum of 9 credit hours can be transferred from a regionally accredited graduate school with department chair’s approval.

Selected List of Elective Course

Required  AI Concentrations

ECE 502 Advanced Python Applications

ECE 667 Deep Learning Applications

in addition to  3 course from the following( for AI concentration):

ECE 510 Machine Learning Fundamentals
ECE 655 Deep Learning Fundamentals
ECE 656 Reinforcement Learning
ECE 657 Natural Language Processing
ECE 660 Parallel Implementation of ML Algorithms with GPUs (Python Mumba programming)
ECE 661 AI application development in Engineering and Science (self-driving cars, computer vision, Games, AI in Cybersecurity)
ECE 662 AI application development in business (Fintech/algorithmic trading)

SWE 645 Blockchain and Peer to Peer applications
ECE 663 Machine Learning Algorithm deployment (dockers and Kubernetes, TensorFlow Lite)

CEN 540 Network Security TechniquesCEN 542 Computer Vision and Image Processing
CEN 548 Computer Network Systems
CEN 556 Distributed Computing Systems
CEN 581 Principle of Internet of Things
EEN 630 Quantum Computing and Systems
EEN 739 Bioelectronics and Bioengineering
EEN 749 Advanced Digital Signal Processing
ECE 758 Advanced IoT System Design
EEN 758 Advanced System Design
EEN 774 Advanced Wireless Communications

Admission Requirements

  • Bachelor’s degree with a minimum GPA of 2.75, or a Master’s degree with a minimum GPA of 3.0.
  • Proof of English proficiency:* All applicants whose native language is not English and who did not receive either a bachelor’s or graduate degree from an English-speaking institution must take one of the following English proficiency tests:
    • Test of English as a Foreign Language (TOEFL) examination: score of 72 or better for the internet-based test (iBT).
    • International English Language Testing System (IELTS) examination: band score of 6.0 or better for the academic module.
    • Duolingo English Test: score of 120 or better out of 160.
  • Demonstrated commitment to contribute to and complete the program

*U.S. citizens or U.S. Permanent Residents who have earned an undergraduate or graduate degree from a regionally accredited institution in the U.S. are waived from this requirement.

*Who has 2+ years of work experience in the United States are waived from this requirement.



should apply

As an ITU computer engineering student, you can:

  • Concentrate on some of the most cutting-edge technologies and their implementations in system design
  • Learn from instructors who come from industry-leading companies like ARM, Fujitsu, and Intel, and receive insider insight into the latest trends
  • Conduct laboratory research in such areas as: Artificial Intelligence, Internet of Things, Bioelectronics, System-on-chip, and more
  • Be part of a STEM program designed to teach students the skills required to thrive in Silicon Valleys ever-evolving tech sector

Learning Outcomes

Explain current and emerging technologies in Chip Design or System Design in electrical engineering.

Engineering Ability
Demonstrate an understanding of established and emerging engineering techniques, and problem-solving skills.

Research Ability
Conduct independent research to solve challenges in electrical engineering.

Career Responsibility
Apply professional ethics in the definition, planning, and execution of engineering projects.

Critical Thinking
Analyze spectrum to make evidence-based choices between various engineering paradigms and alternative options.

Communication Skills
Resent technical issues clearly in oral and written communications.

Support team effort through collaboration to achieve project goals.