Foundations of Data Science II

Foundations of Data Science II

Simons Institute via YouTube Direct link

Brief Idea of Proof

17 of 17

17 of 17

Brief Idea of Proof

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Foundations of Data Science II

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  1. 1 Intro
  2. 2 Application of SVD to Gaussian Mixtures
  3. 3 SVD subspace = space of means
  4. 4 Life without a Stochastic Model: An Example Theorem Hypothesis
  5. 5 Lile without a Stochastic Model An Example
  6. 6 Doing without a stochastic Model
  7. 7 Numerical Algorithms
  8. 8 Why Randomized Algorithms?
  9. 9 Simple Setting
  10. 10 Problems
  11. 11 A little Notation
  12. 12 Low Rank Approximation with Additive Error
  13. 13 Data Handling, Pass efficient Model
  14. 14 Length squared sample of rows and col's suffice
  15. 15 Different Topic: Markov Chains A Markov Chain (MC) is a directed graph with positive edge
  16. 16 Conductance, Rapid Mixing of Symmetric MC's
  17. 17 Brief Idea of Proof

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