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MATH 569B - Linear Algebra for Data Science: Geometric Techniques for Data Reduction

  • 1 credit

Projections, data fitting and over-determined linear systems, eigenvectors and eigenvalues, the spectral theorem for symmetric matrices, data driven bases, principal component analysis, the singular value decomposition. Credit not allowed for both MATH 569B and MATH 580A3 (Linear Algebra for Data Science: Geometric Techniques for Data Reduction).

Prerequisite

MATH 569A (Linear Algebra for Data Science: Matrices and Vectors Spaces).

Instructors

Michael Kirby

9704916850 | kirby@math.colostate.edu

Learn more at: http://www.math.colostate.edu/~kirby