Minors

There’s nothing “minor” about AI.

Now more than ever, writing savvy code and understanding the abilities and limits of AI touches just about every industry there is. And we’re not hoarding our secrets. To empower a broad range of Duke graduates with the skills that today’s top employers are looking for, we offer invaluable minors in ECE as well as Machine Learning & AI.

The latter is available to ECE majors, too (although a concentration in machine learning is likely a better option), and both can be an excellent complement to another major and provide a new layer of knowledge and expertise.

Expand Your Expertise

Minor in ECE

Minor in Machine Learning & AI

Concentrations within ECE

Independent Study

Minor in ECE

The minor in ECE requires a minimum of five technical courses. Three courses must be drawn from the set of “core courses” required of all ECE majors and two must be upper-level ECE courses.

  • Core courses (choose 3): ECE 110L (Fundamentals of ECE), ECE 230L (Microelectronic Devices & Circuits), 250D (Computer Architecture), 270DL (Fields & Waves), and 280L (Signals & Systems)

    • Note that ECE 110L is a prerequisite for the other core courses.
    • Students with credit for any of these courses (e.g., exact or equivalent course taken to satisfy a requirement of their major(s)) may substitute additional upper-level ECE courses. The DUS in ECE must approve such exceptions.

    Upper-level courses (2): Two ECE courses at or above the 300-level

    • At most, one ECE Independent Study (supervised by an ECE faculty member) can be used toward satisfying this requirement.
    • At most, one 300-level (or above) course cross-listed between ECE and the major department can be used toward satisfying this requirement. This course may not be double-counted toward the major.

    Courses that are used to fulfill the student’s major(s) may not be double-counted toward the minor. In addition, ECE courses with content substantially equivalent to courses in the student’s major(s) may not be counted toward the minor.

    It is expected that a student pursuing a minor in ECE will satisfy all pre-requisites for each course selected for their minor program. This will typically involve completion of courses in Math, Physics and/or Computer Science, which are pre-requisites for many of the core ECE courses.

    Students interested in pursuing a Minor in ECE are advised to discuss their plan of study with the Director of Undergraduate Studies in ECE.

Minor in Machine Learning & Artificial Intelligence

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.

  • Fundamental Courses

    Required

    • 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)

    Important Notes

    • 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.

Undergraduate Contacts

Rabih Younes Profile Photo
Rabih Younes Profile Photo

Rabih Younes

Associate Director of Undergraduate Studies, Assistant Professor of the Practice in the Department of ECE