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1
Intro
2
A Brief Description
3
Bootcamp Overview
4
Sums of independent random variables
5
Distributions in Data Science
6
The Theorem
7
Corollaries
8
Exponential, Power Law Distributions
9
Dimension Reduction-two methods
10
Length Preserving Projection
11
Projection with Gaussian Vectors
12
Random Projection Theorem
13
Means Separated by O(1) Standard Deviations
14
Singular Value Decomposition
Description:
Explore the foundations of data science in this 58-minute lecture from the Simons Institute's Data Science Boot Camp. Delve into key concepts presented by David Woodruff and Ravi Kannan of Microsoft Research India, covering topics such as sums of independent random variables, distributions in data science, dimension reduction techniques, and singular value decomposition. Learn about length-preserving projection, projection with Gaussian vectors, and the Random Projection Theorem. Gain insights into exponential and power law distributions, as well as means separated by O(1) standard deviations. Enhance your understanding of fundamental data science principles and their applications in this comprehensive overview.

Intro and Foundations of Data Science I

Simons Institute
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