Introduction of Algorithms For Big Data Compsci 229r Lecture 24
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Algorithms for Big Data (COMPSCI 229r), Lecture 1
Algorithms for Big Data (COMPSCI 229r), Lecture 4
Algorithms for Big Data (COMPSCI 229r), Lecture 25
Advanced Algorithms (COMPSCI 224), Lecture 9
Algorithms for Big Data (COMPSCI 229r), Lecture 23
Algorithms for Big Data (COMPSCI 229r), Lecture 22
Algorithms for Big Data (COMPSCI 229r), Lecture 7
Advanced Algorithms (COMPSCI 224), Lecture 6
Advanced Algorithms (COMPSCI 224), Lecture 18
Algorithms for Big Data (COMPSCI 229r), Lecture 17
Algorithms for Big Data (COMPSCI 229r), Lecture 2
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Last Updated: September 22, 2026
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Summary
Competitive paging, cache-oblivious Sparse JL proof wrap-up, Fast JL Transform, approximate nearest neighbor. More efficient exponential-time MIT 6.100L Introduction to CS and Programming using Python, Fall 2022 Instructor: Ana Bell View the complete course: ... Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' P-stable sketch analysis, Nisan's PRG, ℓp estimation for p MapReduce: TeraSort, minimum spanning tree, triangle counting. Randomized paging, packing/covering linear programs, weak duality, approximate complementary slackness, primal/dual online ... External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. CountSketch, ℓ0 sampling, graph sketching. Amortized analysis, binomial heaps, Fibonacci heaps. second order methods (Newton's method), path-following interior point wrap-up. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. Distinct elements, k-wise independence, geometric subsampling of streams.
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