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Principles and Theory for Data Mining and Machine Learning

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Cover of 'Principles and Theory for Data Mining and Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Variability, Information, and Prediction
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    Chapter 2 Local Smoothers
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    Chapter 3 Spline Smoothing
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    Chapter 4 New Wave Nonparametrics
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    Chapter 5 Supervised Learning: Partition Methods
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    Chapter 6 Alternative Nonparametrics
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    Chapter 7 Computational Comparisons
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    Chapter 8 Unsupervised Learning: Clustering
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    Chapter 9 Learning in High Dimensions
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    Chapter 10 Variable Selection
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    Chapter 11 Multiple Testing
Attention for Chapter 11: Multiple Testing
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