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Matrix Sketching: SVD vs. Eigendecomp. Covariance Summation, Frequent Directions, Row Sampling, Count Sketch.

Slides: https://docs.google.com/presentation/d/1czESwIHkEh2jAXa-c_uGsyUBzJ6SWTIROOmSpUfzTWE/edit?usp=sharing.

Dimensionality Reduction: Define the projection via dot products, and the sum of squared error objective. review SVD, and how ...

Distance Metric Learning: Eigen-domp trick. classifical Multidimensional Scaling (cMDS), Linear Discriminant Analysis (LDA), ...

Slides: https://docs.google.com/presentation/d/1Jq_fl5tzP5zsstKgP24JxzpNS4TM4XwE5pbTK_UaOmY/edit?usp=sharing.

Slides: https://docs.google.com/presentation/d/1fSGI7AcSBZn-xvfuhI_Ba_jwgmbDxasFAzovOMEqwS0/edit?usp=sharing.

Slides: https://docs.google.com/presentation/d/1bMyNFmo0JymeTl7-rN0xPOBQzPw2LluAlfA9L3QtH7g/edit?usp=sharing.