Machine Learning and Artificial Intelligence Minor

Program Code: U-ECE-MLAI
Plan Type: Minor
Department: Pratt School of Engineering, Electrical & Computer Engineering Department
Website: ece.duke.edu/undergrad/degrees/minor/ml-ai 

Program Summary

The Minor in Machine Learning & Artificial Intelligence provides undergraduates with an understanding of the core concepts of ML and AI, as well as a rigorous and coherent exposure to both fundamental theories and applications.

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.

Go to Programs by Department to view all related programs.

Academic Requirements

At least 5 units total to complete the minor. At least 34 units total to earn a degree.

• Fundamental Courses (3 units)

  • ECE 480

  • ECE 580

  • ECE 682D, or ECE 687D

• Electives (2 units)

  • ECE 585

  • ECE 588

  • ECE 661

  • ECE 684

  • ECE 685D

  • COMPSCI 527

  • MATH 412

  • MATH 465

  • STA 340

  • STA 360

Minor requirement details. Courses that are used to fulfill the student’s primary major may not be double-counted toward the minor. Courses with content substantially equivalent to courses in the student's primary major 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 primary major) 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.

Prerequisites. 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 prerequisites for the fundamental and elective courses. Specifically, the following prerequisite knowledge is assumed:

• Mid-level programming course (e.g., COMPSCI 201)

• Linear algebra (e.g., MATH 216, 218, 221)

• Introductory statistics (e.g., EGR 238L, ECE 380, ECE 555, STA/MATH 230, STA 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 prerequisite.