Concentration in Machine Learning

Complete five courses—Earn a notation on your Duke transcript

Students pursuing a major in ECE may focus their course selection within the major by following a defined thematic pathway within the major.

Successful completion of all requirements will result in a designation of "Concentration in Machine Learning" on the official transcript.

The Concentration in ML does not require additional courses

ECE majors can declare this concentration by selecting this option on the Pratt Declaration of Major form

Completion of the Concentration in Machine Learning requires a minimum of 5 courses.

These requirements can be met within the general requirements of the ECE major and do not require any additional courses; however, the choice of classes has been constrained to fit the Concentration requirements.

Requirements

Important Notes

  • The ECE major requirement being fulfilled appears in square brackets: [...]
  • No course may be used to fulfill more than one requirement
  1. ECE 480: Applied Probability for Statistical Learning

    [ECE Concentration Elective #1—Signal Processing, Communications and Control Systems (SPC&C)]

  2. ECE 580: Introduction to Machine Learning

    [ECE Concentration Elective #2—SPC&C]

  3. One of the following

    [ECE Area of Concentration Elective #3—any area, including SPC&C]:

    • ECE 588: Image & Video Processing
    • ECE 661: Computer Engineering Machine Learning & Deep Neural Networks
    • ECE 662: Machine Learning Acceleration & Neuromorphic Computing
    • ECE 663: Machine Learning in Adversarial Settings
    • ECE 682D/CS 571/Stat 561: Probabilistic Machine Learning
    • ECE 684: Natural Language Processing 
    • ECE 685D: Deep Learning
    • ECE 687D/CS 671D/Stat 671D: Machine Learning
    • ECE 590: Special Topic courses on Machine Learning that are approved area of concentration electives (with DUS approval)
  4. One of the following

    [ECE Free Elective]:

    • ECE 588: Image & Video Processing
    • ECE 661: Computer Engineering Machine Learning & Deep Neural Networks
    • ECE 662: Machine Learning Acceleration & Neuromorphic Computing
    • ECE 663: Machine Learning in Adversarial Settings
    • ECE 682D/CS 571/Stat 561: Probabilistic Machine Learning
    • ECE 684: Natural Language Processing 
    • ECE 685D: Deep Learning
    • ECE 687D/CS 671D/Stat 671D: Machine Learning
    • ECE 689/CS 676: Advanced Topics in Deep Learning
    • ECE 590: Special Topic courses on Machine Learning (with DUS approval):
      • ECE 590 (S23): Brain Computer Interfaces
  5. One of the following

    [ECE Extension Elective]:

    • ECE 588: Image & Video Processing
    • ECE 661: Computer Engineering Machine Learning & Deep Neural Networks
    • ECE 662: Machine Learning Acceleration & Neuromorphic Computing
    • ECE 663: Machine Learning in Adversarial Settings
    • ECE 682D/CS 571/Stat 561: Probabilistic Machine Learning
    • ECE 684: Natural Language Processing 
    • ECE 685D: Deep Learning
    • ECE 687D/CS 671D/Stat 671D: Machine Learning
    • ECE 689/CS 676: Advanced Topics in Deep Learning
    • ECE 590: Special Topics courses on Machine Learning (with DUS approval):
      • ECE 590 (S23): Brain Computer Interfaces
    • CompSci 371: Elements of Machine Learning
    • CompSci 527: Computer Vision
    • 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