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Innovations in Machine Learning

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Cover of 'Innovations in Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 A Bayesian Approach to Causal Discovery
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    Chapter 2 A Tutorial on Learning Causal Influence
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    Chapter 3 Learning Based Programming
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    Chapter 4 N-1 Experiments Suffice to Determine the Causal Relations Among N Variables
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    Chapter 5 Support Vector Inductive Logic Programming
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    Chapter 6 Neural Probabilistic Language Models
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    Chapter 7 Computational Grammatical Inference
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    Chapter 8 On Kernel Target Alignment
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    Chapter 9 The Structure of Version Space
Attention for Chapter 4: N-1 Experiments Suffice to Determine the Causal Relations Among N Variables
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