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MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018

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An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis, Signal Processing,...

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7. Eckart-Young: The Closest Rank k Matrix to A

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Lecture 13: Randomized Matrix Multiplication

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12. Computing Eigenvalues and Singular Values

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Lecture 11: Minimizing ‖x‖ Subject to Ax = b

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Lecture 10: Survey of Difficulties with Ax = b

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9. Four Ways to Solve Least Squares Problems

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Lecture 8: Norms of Vectors and Matrices

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6. Singular Value Decomposition (SVD)

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5. Positive Definite and Semidefinite Matrices

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