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
Distinct elements, k-wise independence, geometric subsampling of streams. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 1
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
- Amnesic dynamic programming (approximate distance to monotonicity).
- Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Matrix completion.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 1.
