Understanding Algorithms For Big Data Compsci 229r Lecture 4
If you are looking for information about Algorithms For Big Data Compsci 229r Lecture 4, you have come to the right place. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 4
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Amnesic dynamic programming (approximate distance to monotonicity).
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 4
Analysis of ℓp estimation Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ... Symmetrization, hashing: linear probing (5-wise indep.), bloom filters, cuckoo hashing, bloomier filters.
Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
We hope this detailed breakdown of Algorithms For Big Data Compsci 229r Lecture 4 was helpful.