Media Summary: Distinct elements, k-wise independence, geometric subsampling of streams. Amnesic dynamic programming (approximate distance to monotonicity). Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.
Harvard Csci E63 Big Data - Detailed Analysis & Overview
Distinct elements, k-wise independence, geometric subsampling of streams. Amnesic dynamic programming (approximate distance to monotonicity). Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm. Empirical Drivers of Employee Motivation Using the ELK Stack Spring 2015 by Ioana Boier. This is the full report of my final project for the Spring 2017 semester of Summary Empirical Drivers of Employee Motivation Using the ELK Stack Spring 2015 by Ioana Boier.
Harvard E63 Product Development Big Data Full Presentation Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
