Media Summary: Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. Amnesic dynamic programming (approximate distance to monotonicity).
Algorithms For Big Data Compsci - Detailed Analysis & Overview
Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. Amnesic dynamic programming (approximate distance to monotonicity). Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing. Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspaceĀ ... Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor. Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
