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1
Intro
2
Application of SVD to Gaussian Mixtures
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SVD subspace = space of means
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Life without a Stochastic Model: An Example Theorem Hypothesis
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Lile without a Stochastic Model An Example
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Doing without a stochastic Model
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Numerical Algorithms
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Why Randomized Algorithms?
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Simple Setting
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Problems
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A little Notation
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Low Rank Approximation with Additive Error
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Data Handling, Pass efficient Model
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Length squared sample of rows and col's suffice
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Different Topic: Markov Chains A Markov Chain (MC) is a directed graph with positive edge
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Conductance, Rapid Mixing of Symmetric MC's
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Brief Idea of Proof
Description:
Explore advanced concepts in data science through a comprehensive lecture covering Singular Value Decomposition (SVD) applications, Gaussian mixtures, and stochastic modeling alternatives. Delve into numerical algorithms, randomized approaches, and low-rank approximations with additive error. Gain insights on data handling techniques and pass-efficient models. Conclude with an introduction to Markov Chains, examining conductance and rapid mixing in symmetric Markov Chains. Learn from Microsoft Research India's Ravi Kannan as he presents key topics from the Foundations of Data Science Boot Camp.

Foundations of Data Science II

Simons Institute
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#Mathematics #Algebra #Linear Algebra #Singular Value Decomposition #Data Science #Computer Science #Algorithms #Randomized Algorithms #Statistics & Probability #Probability Theory #Markov Chains #Low-Rank Approximation
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Intro