The Masters of Science in Electrical Engineering (MSEE) degree program focuses on the following areas:
- Chip Design
- System Design
Integrated Circuit (IC) chips constantly bring revolutionary computing power to the world, empowering intelligent and automatic devices. AI chips implement artificial intelligence (AI) algorithms on IC chips to lead advanced technologies in engineering. System designs including embedded systems accomplish Internet of Things (IoT) from distributed systems to data collectors. Computer algorithms, networks, communications, scientific computing, software and coding skills are important knowledge to students for victorious in the field.
As an ITU computer engineering student, you can:
- Get hands-on design experience in embedded systems, integrated circuits, AI chips and applications, etc.
- Learn from instructors who come from industry leading companies like Intel and Google. They bring industry experience into the classroom and provide insight into the latest trends.
- Gain access to research conducted in state-of-the-art labs. Practice on research and design in current research developments.
- Be part of a STEM program designed to teach students the skills required to thrive in Silicon Valley’s ever-evolving tech sector.
- 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 an English proficiency test.
- 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. 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.
Here are our program learning outcomes:
- Fundamentals: 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.
- Teamwork: Support team effort through collaboration to achieve project goals.
Our 36 credit hour curriculum is completed in 16 months. The 36 credit hours are composed of core courses, electives, cross disciplinary electives, capstone or thesis, and an internship.
- AI Design Using FPGA
- Deep Learning Engineering Projects
- Distributed Computing
- Bioelectronics and Bioengineering
- IoT System Design
- IC Design to Silicon
- 4 Core Courses: 12 Credit Hours
- Capstone or Thesis Project: 3 Credit Hours
- Internship: 1 Credit Hour
Elective Courses: 11 – 20 Credit Hours
- Minimum 6 Credit Hours in Electrical Engineering
- Cross-Disciplinary (MBA, EM, or DA) Electives: Up to 3 Credit Hours. Any CE, EE, SE or CS course can be chosen as an elective.
- Transfer Credits: A maximum of 9 credit hours can be transferred from a regionally accredited graduate school with department chair's approval.
- Internship: 1 – 9 Credit Hours
36 Total Credit Hours
Core Courses - Choose four from the list:
- ECE 500 Electrical and Computer Engineering (3)
- ECE 510 Algorithms and Data Analysis (3)
- EEN 511 Integrated Circuit Design (3)
- CEN 551 Computer Architecture (3)
- CEN 556 Distributed Computing Systems (3)
Capstone Course or Thesis- Choose one from the list:
- ECE 646 Capstone Course 1 - IoT System Design (3)
- ECE 655 Capstone Course 3 - Deep Learning Engineering Projects (3)
- CEN 643 Capstone Course 2 - Advanced Image Processing (3)
- EEN 698 Master Thesis I (3)
- EEN 699 Master Thesis II (6)
For more information on program requirments and course descriptions click here.