High-Dimensional Probability and Linear Algebra for Machine Learning

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Key topics from non-asymptotic random matrix theory: Bounds on minimum and maximum singular values of many classes of high-dimensional random matrices, and on sums of a large number of random matrices. Chaining. Other linear algebra and probability concepts commonly used in Theoretical Machine Learning research. Discussion of recent papers in this area.→MATH 6230→unlocks 0 courses

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