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