Mathematics for Artificial Intelligence

Description from DukeHub

Mathematics and numerical algorithms for artificial intelligence and machine learning. Linear algebra: vector spaces; linear maps; orthogonality; singular-value decomposition; linear systems; eigenspaces; determinant; Schur decomposition. Calculus: gradient, Hessian, Jacobian matrix; back-propagation; Optimization: unconstrained minimization; Lagrange multipliers; gradient descent; Newton's method. Applications: principal component analysis; linear regression; rigid transformations; logistic regression classifier. Prerequisite: COMPSCI 101 or 201, and MATH 111L or 112L. Fluency in Python recommended. Not recommended for students who have taken MATH 216, 218, or 221.

Skills

• programmingdepartment: COMPSCI
• data and statisticsdescription: "regression" and 2 more
• ai and machine learningtitle: "artificial intelligence"
• lab workhas a lab component
• makingdescription: "decomposition" and 1 more

AI-resilient: 1 of 5 1/5

• ✓Yes: Physical, lab or field work · "laboratory"
• No: Live work with people
• No: Judgment someone is accountable for
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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