Short Overview: Johns Hopkins University's Alex Szalay (the Alumni Centennial Professor of Astronomy) and Stephanie Reel (Vice Provost for ... Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.

Gibbons Lectures 2020 Big Data 23032 -

Johns Hopkins University's Alex Szalay (the Alumni Centennial Professor of Astronomy) and Stephanie Reel (Vice Provost for ... Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.

Important details found

  • Johns Hopkins University's Alex Szalay (the Alumni Centennial Professor of Astronomy) and Stephanie Reel (Vice Provost for ...
  • Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
  • Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.
  • Associate Professor Ian Watson, Department of Computer Science, the University of Auckland.
  • Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.

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Gibbons Lectures 2017: The Ethics of AI

Gibbons Lectures 2017: The Ethics of AI

Associate Professor Ian Watson, Department of Computer Science, the University of Auckland.

Big Data and the Scientific Revolution

Big Data and the Scientific Revolution

Johns Hopkins University's Alex Szalay (the Alumni Centennial Professor of Astronomy) and Stephanie Reel (Vice Provost for ...

Algorithms for Big Data (COMPSCI 229r), Lecture 2

Algorithms for Big Data (COMPSCI 229r), Lecture 2

Distinct elements, k-wise independence, geometric subsampling of streams.

Algorithms for Big Data (COMPSCI 229r), Lecture 17

Algorithms for Big Data (COMPSCI 229r), Lecture 17

Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.

Algorithms for Big Data (COMPSCI 229r), Lecture 1

Algorithms for Big Data (COMPSCI 229r), Lecture 1

Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.

Algorithms for Big Data (COMPSCI 229r), Lecture 22

Algorithms for Big Data (COMPSCI 229r), Lecture 22

Read more details and related context about Algorithms for Big Data (COMPSCI 229r), Lecture 22.

Beyond Big Data | Matthew Salganik | TEDxPrincetonU

Beyond Big Data | Matthew Salganik | TEDxPrincetonU

Matthew Salganik will describe the tension between readymade data (

Algorithms for Big Data (COMPSCI 229r), Lecture 7

Algorithms for Big Data (COMPSCI 229r), Lecture 7

Read more details and related context about Algorithms for Big Data (COMPSCI 229r), Lecture 7.

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Beyond Big Data

The Oxford Internet Institute is excited to welcome Matthew J. Salganik from Princeton University Department of Sociology for his ...

Algorithms for Big Data (COMPSCI 229r), Lecture 15

Algorithms for Big Data (COMPSCI 229r), Lecture 15

Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.