Introduction to Algorithms For Big Data Compsci 229r Lecture 6
If you are looking for information about Algorithms For Big Data Compsci 229r Lecture 6, you have come to the right place. CountMin sketch, point query,
Algorithms For Big Data Compsci 229r Lecture 6 Comprehensive Overview
Analysis of ℓp estimation CountSketch, ℓ0 sampling, graph sketching. Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 6
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Amnesic dynamic programming (approximate distance to monotonicity).
- Competitive paging, cache-oblivious
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
We hope this detailed breakdown of Algorithms For Big Data Compsci 229r Lecture 6 was helpful.