MATH 218D-1 · also MATH 718D-1
Matrices and Vector Spaces
Soham Karwa
Description from DukeHub
Solving systems of linear equations, matrix factorizations and fundamental vector subspaces, orthogonality, least squares problems, eigenvalues and eigenvectors, the singular value decomposition and principal component analysis, applications to data-driven problems. Intended primarily for students in computer science and other data-focused sciences. Prospective math majors should take Mathematics 221 instead. Not open to students who have taken Mathematics 216 or 221. Recommended prerequisite: Mathematics 21, 121, 106L, or 111L.
Skills
- data and statisticsdepartment: MATH
AI-resilient: 0 of 5
- No: Physical, lab or field work
- 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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