Computer Engineering, M.S.

All options require students to complete the following:

• ECGR 5101 – Advanced Embedded Systems (3)
• ECGR 5181 – Computer Architecture (3)
• ECGR 5187 – Data Communications and Networking II (3)

Select from the following elective courses to complete the degree requirements.  Courses not included in this list, including new and special topics courses on computer engineering, may be eligible with approval from the student’s graduate advisor.  Also, online courses offered through the NCSU Engineering Online program would be eligible.

Note(s):

Students pursuing an optional concentration will replace 12 credits of electives with the concentration requirements.

A maximum of 6 credit hours of transfer credit, including courses taken through NCSU Engineering Online, are permitted.)

• ECGR 5090 – Special Topics (1 to 6)
• ECGR 5100 – Research Tools and Techniques in Computer Engineering (3)
• ECGR 5103 – Machine Vision, AI and Image Processing (3)
• ECGR 5124 – Digital Signal Processing (3)
• ECGR 5133 – VLSI Systems Design (3)
• ECGR 5134 – Advanced VLSI Systems Design (3)
• ECGR 5146 – Introduction to VHDL (3)
• ECGR 5196 – Introduction To Robotics (3)
• ECGR 6090 – Special Topics (1 to 6)
• ECGR 6114 – Digital Signal Processing II (3)
• ECGR 6118 – Applied Digital Image Processing (3)
• ECGR 6119 – Applied Artificial Intelligence (3)
• ECGR 6120 – Wireless Communication and Networking (3)
• ECGR 6146 – Advanced VHDL (3)
• ECGR 6181 – Embedded Operating Systems (3)
• ECGR 6182 – Advanced Embedded Operating Systems (3)
• ECGR 6185 – Embedded Commercial Product Design (3)
• ECGR 6188 – Fundamentals of Wireless Systems and Protocols (3)
• ECGR 6189 – Wireless Sensor Networks (3)
• ITCS 6114 – Algorithms and Data Structures (3)
• ITCS 6151 – Intelligent Robotics (3)
• ITCS 6152 – Robot Motion Planning (3)

The Department of Electrical and Computer Engineering offers a Concentration in Artificial Intelligence and Machine Learning (AI and ML) for the MSCPE program. The concentration requires taking one core course and three courses from a list of approved electives. The Concentration in AI and ML empowers students to design intelligent and autonomous systems across various domains. It emphasizes the application of machine learning and data mining to solve real-world challenges and integrates intelligent behavior into computing platforms.

Machine Learning, a pivotal domain influencing numerous industries, is a key focus of this concentration. Students will acquire the expertise to choose, modify, optimize, or create machine learning algorithms tailored for specific applications. They will also learn to evaluate their efficacy, familiarize themselves with related software and hardware tools, manage and visualize diverse data sets, and interpret methods from the machine learning literature. With the knowledge from the Concentration in AI and ML, students are positioned for success in fields like data mining, robotics, natural language processing, and computer vision.

This concentration delves into topics such as computer vision, natural language processing, robotics, deep learning, and knowledge acquisition. Courses emphasize real-world machine learning applications in engineering systems, offering students a deeper understanding of practical machine learning deployment. Specific focus areas include anomaly detection, defect detection, predictive maintenance, intelligent scheduling, and resource allocation. Additionally, the concentration highlights the application of machine learning and advanced language models to real-world engineering tasks like reasoning, design, validation, and testing. Emerging trends in machine learning, such as generative AI and large pre-trained transformers, which promise to revolutionize future engineering systems, are also covered. The curriculum ranges from foundational subjects to more advanced, application-centric topics within broader engineering systems.

The concentration is reflected in the student’s transcript upon successful completion of the MSCPE program. Students interested in earning their MSCPE degree with the concentration must indicate their interest in this option in their Plan of Study which must be submitted within their second semester into the master’s program. The MSCPE degree can also be earned without specifying a concentration, where the student has greater flexibility in selecting their courses.

In order to earn a MSCPE degree with a Concentration in AI and ML, a student must take:

Core Courses

• ECGR 5105 – Introduction to Machine Learning (3)

Any 3 elective courses from the approved list below, with at least one at the 6000 level

• ECGR 5103 – Machine Vision, AI and Image Processing (3)
• ECGR 5106 – Real-Time Machine Learning (3)
• ECGR 5115 – Convex Optimization and AI Applications
• ECGR 5116 – Artificial Intelligence for Biomedical Applications (3)
• ECGR 5117 – AI for Robotics and Automation (3)
• ECGR 5127 – Machine Learning for the Internet of Things (3)
• ECGR 6116 – Foundations of Reinforcement Learning and Optimal Control (3)
• ECGR 6119 – Applied Artificial Intelligence (3)
• ECGR 6126 – Optimization for Machine Learning (3)

In addition to the requirements above, students seeking a graduate concentration must also complete the general requirements for the MSCPE degree for their chosen option (thesis, project, or course-only exam option). It is noted that the concentration courses may be used to fulfill the MSCPE degree requirements.