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. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
Algorithms For Big Data Compsci 229r Lecture 1 Comprehensive Overview
Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
Matrix completion.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 1
- Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
- Amnesic dynamic programming (approximate distance to monotonicity).
- ORS theorem (distributional JL implies Gordon's theorem), sparse JL.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 1.
