Understanding Algorithms For Big Data Compsci 229r Lecture 25
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 25. MapReduce: TeraSort, minimum spanning tree, triangle counting.
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 25
- Analysis of ℓp estimation
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Matrix completion.
- Zeta transform, Möbius inversion, streaming
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 25
Competitive paging, cache-oblivious External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 25.
