Learn how intelligent systems are designed, trained and deployed in real-world environments through the ECE ML/AI master’s track. You can tailor your coursework toward areas such as deep learning, natural language processing and data engineering while learning from faculty with strengths in machine learning theory, AI systems and interdisciplinary applications.
Note: Courses are subject to change based on availability.
To earn a master’s in ECE focused on machine learning and AI (MS or MEng)
10
Courses
Build depth in machine learning, data engineering and deep learning
Theory + Application
Learn how ML models work and how to use them effectively
Customize Your Degree
Flexible pathways allow you to tailor your curriculum for industry or research
What is the Machine Learning & AI Master’s Track?
Duke ECE’s Machine Learning & AI track gives you a strong foundation in the theory and computational methods behind modern AI. Just as important, it helps you understand how machine learning systems perform in real-world settings, from health care to intelligent devices and other large-scale engineering applications.
Strong foundation in machine learning: Rigorous coursework in programming, mathematics for machine learning, data engineering and deep learning helps you build the technical depth needed to understand and develop modern AI systems.
Learn principles, not just tools: Today’s AI engineers need more than familiarity with programming languages and ML frameworks. In this track, you’ll learn how models work, how to evaluate their performance and how to choose the right methods for new problems as technologies evolve.
Apply AI across disciplines: Tailor your coursework to areas such as natural language processing, computer vision, generative AI, privacy and security, or other advanced topics. You’ll graduate prepared to apply AI across diverse domains, from software and data systems to edge computing and intelligent, resource-constrained devices.
ML/AI Track Curriculum
Graduates must take a minimum of 10 required graduate level courses (30 credits) and the first-year seminar.
ECE 551D (Programming, Data Structures, and Algorithms in C++)
ECE 590—P4ML (Programming and Data Structures for Machine Learning)
If you believe your programming skills are good enough to bypass the programming course requirement, please fill out this application quiz by August 1. (NOTE: The quiz will be available by July 21). Your answers will be reviewed, and you will be contacted after the deadline. If approved, you will receive a waiver for that course, then you may replace this requirement with a course from the Graduate Level Electives category.
ECE 590—MML (Mathematics for Machine Learning)
ECE 590—PML (Practical Machine Learning)
ECE 685D (Introduction to Deep Learning)
ECE 590—DE (Data Engineering)
ECE 565 (Performance Optimization & Parallelism)
ECE 581 (Random Signals and Noise)
ECE 583 (Data Science)
ECE 585 (Signal Detection & Extraction Theory)
ECE 586D (Vector Space Methods with Applications)
ECE 587 (Information Theory)
ECE 662 (ML Acceleration of Neuromorphic Computing)
ECE 663 (Machine Learning in Adversarial Settings)
ECE 682D (Probabilistic Machine Learning)
ECE 684 (Natural Language Processing)
ECE 687D (Theory and Algorithms for Machine Learning)
ECE 689 (Advanced Topics in Deep Learning)
ECE 590 (Graphical Models & Inference)
ECE 590 (AI Security and Privacy)
ECE 590 (Brain Computer Interfaces)
ECE 590 (Systems for Machine Learning)
ECE 590 (AI for Photonics)
ECE 590 (Robot Learning)
ECE 741 (Compressed Sensing and Related Topics)
ECE 790 (Diffusion-based Generative Models)
ECE 590/790 in AI (AI-related advanced topics in ECE with track’s approval)
MS students choose three electives, and MEng students choose one elective from the following list:
Any depth elective from the ML/AI study track not already used to satisfy another requirement
ECE 899 (Independent Study) in a relevant topic with faculty agreement required (max 2)
Any other graduate-level course with advisor approval
Ungraded research (3 credits for optional MS project, 6 credits for optional MS thesis)
ECE 701S (weekly seminar required for first-year master’s students)
MEng 540 (Management of High Tech Industries)
MEng 570 (Business Fundamentals for Engineers)
Internship (0 credits), typically completed in the summer between first and second years
MS students have three options to complete their requirements.
Coursework only (30 credits) with poster session
Coursework (27 credits) + capstone project (3 credits of ungraded research or independent study ECE 899)
Coursework (24 credits) + thesis (6 credits of ungraded research or independent study ECE 899)
Hear From Our Alumni
One aspect I especially appreciated was the flexibility of the program. This freedom to shape my own academic path was incredibly empowering and directly led me to meaningful research experiences, helping me transition to a true research trajectory.
The program’s interdisciplinary nature allowed me to serve as a TA and RA at Fuqua and MIDS, bridging technology with business and data science. This unique combination opened a new perspective on interdisciplinary finance and launched a second growth curve for my career, ultimately empowering me to secure a cutting-edge role in AI agents.
All Duke ECE master’s students have access to professional development support, faculty mentorship and career advising as they prepare for roles in industry or further graduate study. MEng students complete an industry internship and take core courses in management and business fundamentals designed to strengthen career readiness.
Rhodes Family Distinguished Professor of Electrical and Computer Engineering
Rhodes Information Initiative at Duke (iiD)
A university-wide initiative that supports data-driven and AI-enabled research across disciplines, connecting students with collaborators, tools and projects across Duke.
A major Duke-led research institute funded by NSF and DHS, focuses on developing edge computing with groundbreaking AI functionality that leverages next-generation communications networks to provide previously impossible services at reasonable cost.
Duke Artificial Intelligence & Machine Learning Research
We have built a team of internationally recognized experts in artificial intelligence and machine learning. Our engineers urge computer hardware to higher levels of performance by efficiently allocating the computing resources that machine learning applications require, allowing us to harness the power of data to improve health care, enhance security and automation, and advance computer vision.
Duke ECE is part of an innovation ecosystem at Duke University that includes frequent collaborations with other engineering disciplines, the Department of Computer Science, and other universities through the Athena AI Institute. Partnerships with Duke Health allow students to work on medical technologies in real-world environments.
Frequently Asked Questions (FAQs)
A machine learning and artificial intelligence master’s can prepare you to work in sectors including AI and machine learning, software and cloud computing, data analytics, health care technology, intelligent systems and startups. Duke ECE graduates from this track have gone on to work at companies such as Google, Meta, IBM, Microsoft and NVIDIA in roles including machine learning engineer, AI engineer, data engineer, data scientist, software engineer and computer vision engineer.
In Duke ECE’s ML/AI track, you’ll build skills in programming, mathematics for machine learning, data engineering, deep learning and model evaluation. You can also tailor your coursework toward areas such as natural language processing, computer vision, generative AI, or privacy and security, helping you learn both the theory behind AI and how to apply it effectively in real-world systems.
Duke ECE’s machine learning and artificial intelligence track gives you a strong engineering foundation in the theory, computational methods and systems behind modern AI. Although data science or applied AI programs may focus more on analysis tools or domain-specific applications, this track emphasizes how AI models are built, trained, evaluated and deployed in real-world engineering settings.
A new curriculum in the master’s program in Electrical and Computer Engineering’s Machine Learning and Big Data study track will debut in Fall 2025, aligning student training with current industry needs.
ECE graduate student Matthew LaRosa is using machine learning and generative AI to understand strategies for making rapid decisions in complex environments.
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