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). Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor. Distinct elements, k-wise independence, geometric subsampling of streams. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
MapReduce: TeraSort, minimum spanning tree, triangle counting. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p Krahmer-Ward proof, Iterative Hard Thresholding. Logistics, course topics, word RAM, predecessor, van Emde Boas, y-fast tries. Please see Problem 1 of Assignment 1 at ... Today we'll be covering the 7 most important
