Introduction to Algorithms For Big Data Compsci 229r Lecture 1
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 1. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
Algorithms For Big Data Compsci 229r Lecture 1 Comprehensive Overview
This is the first part of a series of So, now we have finished 2 weeks in this course on Titus Brown Random
Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 1
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Krahmer-Ward proof, Iterative Hard Thresholding.
- Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma.
- Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor.
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 1.