Electrical & Computer Engineering Department

Master of Science
in Electrical and 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), and 5G wireless communications are changing many aspects of daily life, including driving, enter-tainment, communication, health care, and virtual and robotic assistants. These changes are creating many new engineering jobs in the fields of AI chip design, smart edge-device design, IoT system design, and intelligent system design.

The department’s chip design environment conforms to industry standards and gives you the practical training you need for your career. Advanced design and computation projects will help you hone your hands-on techniques and skills. You’ll also develop innovation and entrepreneurship-focused thinking through cutting-edge research. The MSEE 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.

Curriculum

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 hours)
  • Nugget courses (up to 3 credit hours)
    • ITU Presents (1 credit hour)
    • ITU Nuggets (2 credit hours)

Core Courses:

ECE 557 Artificial Intelligence and Machine Learning Application
ECE 502 Advanced Python Applications
EEN 541 Digital Signal Processing and System Analysis

Elective Courses: 15 Credit Hours

  • Field Relevant Courses in the ECE Department.
  • Internship: 1-8 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 Courses (see the course catalog for full list)

ECE 510 Machine Learning Fundamentals
ECE 502 Advanced Python Applications
ECE 655 Deep Learning Fundamentals (Keras/TensorFlow 2.0, Pytorch)
ECE 656 Reinforcement Learning (Pytorch)
ECE 657 Natural Language Processing (Keras/TensorFlow 2.0, Pytorch)
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, AI in Cybersecurity)
ECE 662 AI application development in business (Fintech/algorithmic trading)
ECE 663 Machine Learning Algorithm deployment (dockers and Kubernetes, TensorFlow Lite)
CEN 540 Network Security Techniques
CEN 542 Computer Vision and Image Processing
CEN 548 Computer Network Systems
CEN 556 Distributed Computing Systems

Admission Requirements

  • Bachelor’s degree with a minimum GPA of 2.75 or a master’s degree with a minimum GPA of 3.0.
  • Test of English as a Foreign Language (TOEFL) examination; Score of 72 or better for the internet-based test (iBT). 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.
  • 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.

 

 

 

Who
should
apply

As an ITU electrical engineering student, you can:

  • Concentrate on some of the most exciting technologies in the world today, such as VLSI design, analog, signal processing and communication, and system design
  • Implement design specifications and solve engineering problems through analysis, experimentation, and verification of ideas
  • Conduct research in ITUS laboratory facilities in areas like artificial intelligence, green energy, bioelectronics, and more
  • Get hands-on design and research experience utilizing EDA tools from Synopsys, Cadence, Mentor, and free chip design tape-out and chip packaging through MOSIS

What You Will Learn

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.

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.