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