Our focus on data analysis and machine learning provides master's students with the tools to manage, interpret and gain new insights from data
Duke faculty rank among the top 10 in the world in AI/machine learning research. Studying in Duke ECE, you will gain a thorough grounding in the mathematical foundations of Big Data, training in practical programming, and instruction in machine learning, statistics and information theory.
As a student, you'll join a vibrant community rich in cross-campus initiatives focused on data science and machine learning, such as the Rhodes Information Initiative at Duke (iiD).
The Duke ECE Machine Learning/Big Data Track is available as part of:
- A research-oriented Master of Science (MS) degree
- An industry-focused Master of Engineering (MEng) degree
Duke Engineering offers additional master's degree options focused on data analytics and machine learning, including a Master of Engineering Management and degrees in biomedical, civil/environmental and risk engineering. Learn more »
Key Courses
- ECE 551: Programming, Data Structures, and Algorithms in C++
- ECE 580: Introduction to Machine Learning
- ECE 581: Random Signals and Noise
- ECE 586: Vector Space Methods with Applications
- ECE 590: Various Application Courses (multiple listings)
- ECE 682D: Probabilistic Machine Learning
- ECE 685D: Deep Learning
- ECE 687D: Theory of Machine Learning
Other Notable Courses
- ECE 555: Probability for ECE
- ECE 585: Signal Detection and Extraction Theory
- ECE 587: Information Theory
- ECE 588: Image and Video Processing
- ECE 651: Software Engineering
- ECE 661: CE ML and Deep Neural Nets
- ECE 684: Natural Language Processing
- Courses from other departments in Engineering or Sciences (CEE, ME, BME, STA, CS, etc)
"The fundamental software skills and advanced machine learning techniques I learned at Duke are essential for my current job, especially when I'm doing work related to building ML models."
Wanxin Yuan
Software Engineer, Google
Graduate Profiles and Graduate Outcomes »