Foundations of Data Science – Lecture 8 – Low Rank Approximation (LRA) via Length Squared Sampling
Episode details
Episode description
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/