BS Concentration in AI and Machine Learning

Concentration in AI and Machine Learning - Academic Requirements

We have revised our BS degree requirements for the AI/ML Concentration to include our new COMPSCI 270 - Math in AI as a core course. First-year students who matriculate at Duke in Fall 2026 will be subject to these major changes.

NOTE: 2 of the 3 electives MUST be COMPSCI courses.

Students who matriculated at Duke prior to Fall 2026 are grandfathered into the old concentration requirements HERE.

Students must successfully complete at least 15 required units to finish the Computer Science major. Degree candidates must successfully complete at least 34 units total to earn the Bachelor of Science degree.

Prerequisite Courses (4 units) from the following:

  1. Introductory Course - COMPSCI 101L* OR COMPSCI 102L OR COMPSCI 116 OR equivalent
  2. Math Requirement - MATH 111L & MATH 112L OR MATH 21 & MATH 22 / Other equivalents may also count.
  3. Math/Stats Requirement - one of the following:
    • STA 230/MATH 230
    • STA 230S/MATH 230S
    • STA231/MATH 340
    • MATH 231
    • STA 240L

*NOTE: We will waive the COMPSCI 101L requirement, if you successfully take and complete COMPSCI 201.

Core Courses (5 units) from the following:

  1. COMPSCI 201
  2. COMPSCI 230 OR COMPSCI 231D OR COMPSCI 232 see substitutions
  3. COMPSCI 210D OR COMPSCI 250D
  4. COMPSCI 270 OR two courses:
    • one of MATH 202, MATH 212 or MATH 219
    • AND one of MATH 216, MATH 218 or MATH 221
  5. COMPSCI 330

Systems Course (1 unit) - one of the following:

  • COMPSCI 310 OR COMPSCI 510
  • COMPSCI 316 OR COMPSCI 516
  • COMPSCI 345
  • COMPSCI 350L OR COMPSCI 550
  • COMPSCI 351 OR COMPSCI 581
  • COMPSCI 356 OR COMPSCI 514
  • COMPSCI 512

Artificial Intelligence Course (1 unit) - one of the following:

  • COMPSCI 370
  • COMPSCI 570

Machine Learning Core Course (1 unit) - one of the following:

  • COMPSCI 371
  • COMPSCI 372
  • COMPSCI 571 (cross STA 561, ECE 682)
  • COMPSCI 671D (cross STA 671, ECE 687)

Concentration Electives (3 units)

3 COMPSCI courses - 200-level or higher, with QC code from the machine learning list above AND/OR the list below

  • COMPSCI 260 - Intro to Computational Genomics OR COMPSCI 561 - Computational Sequence Biology
  • COMPSCI 290 - Special Topics on the following subjects (some may not be offered regularly):
    • Reinforcement Learning
    • Computational Imaging
    • Intro to Applied Machine Learning
  • COMPSCI 323 - Computational Microeconomics OR COMPSCI 590 - Topics on Computational Microeconomics: Game Theory, Social Choice, and Mechanism Design
  • COMPSCI 362 - Intro to Computational Imaging
  • COMPSCI 375 - Intro to Natural Language Processing
  • COMPSCI 376 - Computational Approaches to Language Processing (cross LINGUIST 399)
  • COMPSCI 390 - Special Topics on the following subjects (some may not be offered regularly):
    • Algorithmic Foundations of Data Science
    • Intro to Applied Machine Learning
    • Intro to Computational Connectomics
    • Deep Connectomics
  • COMPSCI 390A - Conversational AI - Build your own Chatbot
  • COMPSCI 474 - Data Science Competition
  • COMPSCI 526 - Data Science
  • COMPSCI 527 - Computer Vision
  • COMPSCI 575 - Introduction to Natural Language Processing, renumbered from COMPSCI 572 in Fall 2026
  • COMPSCI 590 - Special Topics on the following subjects (some may not be offered regularly):
    • Algorithmic Aspects of Machine Learning
    • Data Science Concepts and Applications
    • Elements of Deep Learning
    • Generative Models
    • Reinforcement Learning
    • Robot Learning
    • Theory for Machine Learning
    • Generative AI in Protein Design
    • Theory of Deep Learning
    • Generative Models: Foundations and Applications
    • Systems for Machine Learning
    • Building Intelligent Agents with Frontier Models
    • Large Language Models
    • Deep Learning
    • Algorithms for Sequential Decision Making - MDPs, Reinforcement Learning, Decision and Game Theory
  • COMPSCI 675D - Intro to Deep Learning (cross ECE 685D)
  • ECON 490 - Selected Topics in Economics: Causality and ML in Econ, taught in Spring 2026
  • MATH 412 - Topological Data Analysis
  • MATH 465 - Introduction to High Dimensional Data Analysis (cross COMPSCI 445)
  • MATH 466 - Math of Machine Learning
  • MATH 541/STA 621 - Applied Stochastic Processes

**NOTE: Of these two, only STA 332/MATH 343 can be used to satisfy the BS requirements.

  • STA 325 - Machine Learning and Data Mining
  • STA 332** - Statistical Inference (cross MATH 343), renumbered from STA 432 in Fall 2025 OR ECE 480 - Applied Probability for Statistical Learning
  • STA 360 - Bayesian Inference
  • one independent study course which has a substantial emphasis related to artificial intelligence or machine learning within COMPSCI, MATH, STA, ECE, or a related area approved by the Director of Undergraduate Studies

A comprehensive list of elective courses is available to students in Stellic, the degree audit system.

To add, drop, or change a concentration, complete the Academic Plan Change form.

Academic Plan Change(link opens in a new window/tab)(link opens in a new window/tab)

Eligibility for specific substitutions may vary by academic plan. A consultation with the Director of Undergraduate Studies is required before final approval can be granted to receive credit for a substitution. 

See the CS Course Substitutions Guide for possible course substitutions which have been pre-approved by the DUS.

Course Substitutions Guide

Departmental Graduation with Distinction

A program for Graduation with Distinction (GWD) in Computer Science is available. Candidates for a degree with Distinction, High Distinction, or Highest Distinction must apply to the Director of Undergraduate Studies (DUS) and meet certain criteria.

See the CS Graduation with Distinction webpage for additional information, resources, and how to apply.

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