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

  • 1 credit
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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)

Important Information

For more information about this course, please contact Academic Success Coordinator, Paige Kanatous.

Textbooks and Materials

Please check the CSU Bookstore for textbook information. Textbook listings are available at the CSU Bookstore about 3 weeks prior to the start of the term.
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