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这里是 ShowMeAI 持续分享的【开源eBook】系列!内容覆盖机器学习、深度学习、数据科学、数据分析、大数据、Keras、TensorFlow、PyTorch、强化学习、数学基础等各个方向。整理自各平台的原作者公开分享(审核大大请放手) ◉ 简介:作者 Sanjiv Ranjan Das 是 Santa Clara 大学商学院金融与数据科学教授。这本书是作者为其课程《Machine Learning with R》开发整理的课堂笔记。 ◉ 目录: 数据科学的艺术 起步:数学基础 开源:R语言建模 更多:数据处理与其他 方差均值:马科维茨优化 从经验中学习:贝叶斯定理 自然语言:从新闻中提取信息 巴斯模型 提取维度:判别和因子分析 竞标:拍卖 截断和估计:有限的因变量 乘风破浪:傅里叶分析 建立联系:网络理论 统计大脑:神经网络 聚类分析和预测树
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S A N J I V R A N J A N D A S
D ATA S C I E N C E :
T H E O R I E S ,
M O D E L S ,
A L G O R I T H M S , A N D
A N A LY T I C S
S . R . D A S
Copyright © 2013, 2014, 2016 Sanjiv Ranjan Das
published by s. r. das
http://algo.scu.edu/∼sanjivdas/
Licensed under the Apache License, Version 2.0 (the “License”); you may not use this book except in compliance
with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0. Unless
required by applicable law or agreed to in writing, software distributed under the License is distributed on an “as
is” basis, without wa rranties or conditions of any kind, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
This printing, July 2016
T H E F U T U R E I S A L R E A DY H E R E ; I T ’ S J U S T N O T V E R Y E V E N LY D I S T R I B U T E D .
– W I L L I A M G I B S O N
T H E P U B L I C I S M O R E FA M I L I A R W I T H B A D D E S I G N T H A N G O O D D E S I G N . I T I S , I N
E F F E C T, C O N D I T I O N E D T O P R E F E R B A D D E S I G N , B E C A U S E T H AT I S W H AT I T L I V E S
W I T H . T H E N E W B E C O M E S T H R E AT E N I N G , T H E O L D R E A S S U R I N G .
– PAU L R A N D
I T S E E M S T H AT P E R F E C T I O N I S AT TA I N E D N O T W H E N T H E R E I S N O T H I N G L E F T T O
A D D, B U T W H E N T H E R E I S N O T H I N G M O R E T O R E M O V E .
– A N T O I N E D E S A I N T- E X U P É R Y
. . . I N G O D W E T R U S T, A L L O T H E R S B R I N G D ATA .
– W I L L I A M E D W A R D S D E M I N G
Acknowledgements: I am extremely grateful to the following friends, stu-
dents, and readers (mutually non-exclusive) who offered me feedback
on these chapters. I am most grateful to John Heineke for his constant
feedback and continuous encouragement. All the following students
made helpful suggestions on the manuscript: Sumit Agarwal, Kevin
Aguilar, Sankalp Bansal, Sivan Bershan, Ali Burney, Monalisa Chati, Jian-
Wei Cheng, Chris Gadek, Karl Hennig, Pochang Hsu, Justin Ishikawa,
Ravi Jagannathan, Alice Yehjin Jun, Seoyoung Kim, Ram Kumar, Fed-
erico Morales, Antonio Piccolboni, Shaharyar Shaikh, Jean-Marc Soumet,
Rakesh Sountharajan, Greg Tseng, Dan Wong, Jeffrey Woo.
Contents
1 The Art of Data Science 25
1.1 Volume, Velocity, Variety 27
1.2 Machine Learning 29
1.3 Supervised and Unsupervised Learning 30
1.4 Predictions and Forecasts 30
1.5 Innovation and Experimentation 31
1.6 The Dark Side 31
1.6.1 Big Errors 31
1.6.2 Privacy 32
1.7 Theories, Models, Intuition, Causality, Prediction, Correlation 37
2 The Very Beginning: Got Math? 41
2.1 Exponentials, Logarithms, and Compounding 41
2.2 Normal Distribution 43
2.3 Poisson Distribution 43
2.4 Moments of a continuous random variable 44
2.5 Combining random variables 45
2.6 Vector Algebra 45
2.7 Statistical Regression 48
2.8 Diversification 49
2.9 Matrix Calculus 50
2.10 Matrix Equations 52
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