Introduction to Algorithms For Big Data Compsci 229r Lecture 17
If you are looking for information about Algorithms For Big Data Compsci 229r Lecture 17, you have come to the right place. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
Algorithms For Big Data Compsci 229r Lecture 17 Comprehensive Overview
Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing. Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ... Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
CountSketch, ℓ0 sampling, graph sketching.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 17
- Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma.
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
- Big Data
- Kind of a handy thing to have but of course you know we live in the world of data
- Path-following interior point, first order methods (gradient descent).
We hope this detailed breakdown of Algorithms For Big Data Compsci 229r Lecture 17 was helpful.