Duration: 1:10:30

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9

Modern data often consists of feature vectors with a large number of features. High-dimensional geometry and Linear Algebra (Singular Value Decomposition) are two of the crucial areas which form the mathematical foundations of Data Science. This mini-course covers these areas, providing intuition and rigorous proofs. Connections between Geometry and Probability will be brought out.

See more at https://www.microsoft.com/en-us/research/video/foundations-of-data-science-lecture-8-low-rank-approximation-lra-via-length-squared-sampling/