Exploring Algorithms For Big Data Compsci 229r Lecture 3
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 3.
- Distinct elements, k-wise independence, geometric subsampling of streams.
- Analysis of ℓp estimation
- Competitive paging, cache-oblivious
- Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma.
- Amnesic dynamic programming (approximate distance to monotonicity).
In-Depth Information on Algorithms For Big Data Compsci 229r Lecture 3
P-stable sketch analysis, Nisan's PRG, ℓp estimation for p Hashing: load balancing, k-wise independence, chaining, linear probing. External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 3.
