Complement your major with a minor in the field of ML/AI
The Minor in Machine Learning & Artificial Intelligence requires the completion of a minimum of five (5) technical courses.
This education offering is an outgrowth of Duke ECE's global research leadership in AI and machine learning.
To provide sufficient foundational breadth, three (3) courses are drawn from identified core areas fundamental to the discipline. Students tailor their course of study through selecting two (2) upper-level (300-level or above) focus courses
- Intermediate Statistics/Probability—ECE 480: Applied Probability for Statistical Learning
- Introductory Machine Learning & Artificial Intelligence—ECE 580: Introduction to Machine Learning
- Intermediate Machine Learning & Artificial Intelligence course—ECE 682D/CS 571D/Stat 561D or ECE 687D/CS 671D/STA 671D
Upper-Level Focus Courses
Choose two (2)
- ECE 585: Signal Detection and Extraction Theory
- ECE 588: Image & Video Processing
- ECE 661: Computer Engineering Machine Learning & Deep Neural Networks
- ECE 662: Machine Learning Acceleration & Neuromorphic Computing
- ECE 684: Natural Language Processing
- ECE 685D: Deep Learning
- CompSci 527: Computer Vision
- Math 412: Topological Data Analysis
- Math 465/CompSci 445: Introduction to High Dimensional Data Analysis
- Stat 340: Introduction to Statistical Decision Analysis
- Stat 360: Bayesian Inference and Modern Statistical Methods
- ME 555 (F23): Robot Learning
- BME 590 (F23): Machine Learning in Pharmacology
- ECE 590: Special Topics courses on machine learning and artificial intelligence topics (with DUS approval)
- Courses that are used to fulfill the student’s major(s) may not be double-counted toward the minor
- Courses with content substantially equivalent to courses in the student's major(s) may not be counted toward the minor
- Students with credit for any of the Fundamental Courses (e.g., exact or equivalent course taken to satisfy a requirement of the major(s)) may substitute additional Upper-Level Focus courses from the approved list above. The Director of Undergraduate Studies in ECE must approve such exceptions.
- At most, one Independent Study course (approved the DUS in ECE) may be used to fulfill one of the upper-level elective requirements.
It is expected that a student pursuing a Minor in Machine Learning & Artificial Intelligence will satisfy all prerequisites for each course selected for their minor program.
This will typically involve completion of courses in Math, Statistics, and Computer Science, which are pre-requisites for the fundamental and elective courses.
Specifically, the following prerequisite knowledge is assumed:
- Mid-level programming course (e.g., CS 201)
- Linear algebra (e.g., Math 216, 218, 218D-2, 221)
- Introductory statistics (e.g., EGR 238L, ECE 380, ECE 555, Stat/Math 230, Stat 240L)
Exceptions may be granted by the Director of Undergraduate Studies in ECE, for example, if a student’s preparation is deemed equivalent to the pre-requisite.