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.
Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 1
- Competitive paging, cache-oblivious
- RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
- Matrix completion.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 1.
