Exploring Algorithms For Big Data Compsci 229r Lecture 2
Welcome to our comprehensive guide on Algorithms For Big Data Compsci 229r Lecture 2.
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
- Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
In-Depth Information on Algorithms For Big Data Compsci 229r Lecture 2
Distinct elements, k-wise independence, geometric subsampling of streams. Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. Matrix completion. Titus Brown Random
Krahmer-Ward proof, Iterative Hard Thresholding.
In summary, understanding Algorithms For Big Data Compsci 229r Lecture 2 gives us a better perspective.
