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