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On Computational Thinking, Inferential Thinking and Data Science
Published on Nov 28, 20165180 Views
The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational scien
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Chapter list
On Computational Thinking, Inferential Thinking and “Data Science”00:00
A Job Description, circa 201501:04
Some Challenges Driven by Big Data08:22
The Challenges are Daunting08:25
Outline10:38
Part 1: Inference and Privacy11:07
Privacy and Data Analysis11:14
Privacy12:39
Inference14:30
Privacy and Inference14:54
Background on Inference - 115:33
Background on Inference - 215:42
Local Privacy17:32
Differential Privacy17:54
Private Minimax Risk19:14
Vignette: Private Mean Estimation20:16
Vignette: mean Estimation - 121:01
Vignette: mean Estimation - 221:46
Vignette: mean Estimation - 322:59
Additional Examples23:01
Computation and Inference23:17
Computation and Inference: Mechanisms and Bounds24:14
A Variational Framework for Accelerated Methods in Optimization26:04
Accelerated gradient desscent26:24
The acceleration phenomen29:27
Accelerated methods - 130:34
Accelerated methods - 230:46
Accelerated methods: Continuous time perspective31:08
Bregman Lagrangian - 133:34
Bregman Lagrangian - 235:42
General convergence rate36:37
Polynomial convergence rate37:19
Time dilation property38:13
Time dilation for general Bregman Lagrangian39:16
Naive discretization doesn't work40:43
Modified discretization41:20
Recap42:34
Summary44:31