Exploring Algorithms For Big Data Compsci 229r Lecture 3
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 3.
- Hashing: load balancing, k-wise independence, chaining, linear probing.
- Distinct elements, k-wise independence, geometric subsampling of streams.
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Competitive paging, cache-oblivious
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
In-Depth Information on Algorithms For Big Data Compsci 229r Lecture 3
Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p Analysis of ℓp estimation Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
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That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 3.