Access cutting-edge technology and learn from industry professions in ITU’s Computer Engineering program. ITU oers a practical education while allowing students the opportunity to work with innovative EDA tools. Our Computer Engineering program oers curriculum and training in one of the most marketable fields in the world today. Expand and perfect your computer engineering skills at ITU, and be ready to apply them instantly in Silicon Valley.
As an ITU Computer Engineering student:
• Get hands-on research experience in embedded systems, green energy, and AI robotics.
• 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. Our partnerships with schools across the world, like Peking University in China, also oer a global perspective 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: Outline up-to-date technologies in computer engineering.
- Engineering Ability: Demonstrate an understanding of established and emerging engineering techniques, and problem-solving skills.
- Research Ability: Solve problems in engineering through self-learning and research activities.
- Career Responsibility: Apply professional ethics in the definition, planning, and execution of engineering projects.
- Critical Thinking: Demonstrate the ability to make evidence-based choices between various engineering paradigms and alternative options for problem solving.
- Communication Skills: Present technical issues clearly in oral and written communications.
- Team Work: Provide support for team projects in a way that promotes effective team dynamics to achieve team 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.
- Distributed Computing
- Deep Learning Engineering Projects
- Advanced Digital Image Processing
- Bioelectronics and Bioengineering /li>
- Principle of Internet of Things
- IoT System Design
- 4 Core Courses: 12 Credit Hours
- Capstone Course: 3 Credit Hours, OR Master Thesis: 3 Credit Hours (Approval is required), OR Master Thesis: 6 Credit Hours (Publication is required)
- Internship: 1 Credit Hour
Elective Courses: 11 – 20 Credit Hours
- Minimum 6 Credit Hours in Computer Engineering
- Cross Disciplinary (MBA, EM, or DA) Electives: Up to 3 Credit Hours. Any course of 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 courses from the list:
- ECE 500 Electrical and Computer Engineering (3)
- ECE 510 Algorithms and Data Analysis (3)
- CEN 551 Computer Architecture (3)
- CEN 556 Distributed Computing Systems (3)
- EEN 511 Integrated Circuit Design (3)
- CEN 699 Master Thesis Research II (6)
Capstone Course or Thesis- Choose one from the list:
- ECE 646 Capstone Course 1 - IoT System Design (3)
- CEN 643 Capstone Course 2 - Advanced Image Processing (3)
- ECE 655 Capstone Course 3 - Deep Learning Engineering Projects (3)
- CEN 698 Master Thesis I (3)
- CEN 699 Master Thesis Research II
For more information on program requirments and course descriptions click here.