Linear Algebra
Vectors, matrices, linear transformations, determinants, eigenvalues/eigenvectors, and orthogonality. The mathematical language of systems and transformations.
Course Overview
Textbook |
OpenStax Introductory Linear Algebra |
Chapters |
TBD |
Sections |
TBD |
Prerequisites |
|
Target |
Foundation for Real Analysis, Abstract Algebra, Differential Geometry, ML |
Status |
Planned |
Chapters will be scaffolded when this course becomes active.
Why This Course
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Machine learning — every neural network is matrix multiplication
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Cryptography — Hill cipher, lattice-based crypto, error-correcting codes
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Networking — adjacency matrices for graph/topology analysis
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Computer graphics — all transformations are matrices
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Data science — PCA, SVD, least squares regression
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Quantum computing — state vectors, unitary transformations