Machine Learning: An Applied Mathematics Introduction
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Description
Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics - K Nearest Neighbours; K Means Clustering; Naïve Bayes Classifier; Regression Methods; Support Vector Machines; Self-Organizing Maps; Decision Trees; Neural Networks; Reinforcement Learning Contents Prologue Chapter 1 - Introduction Chapter 2 - General Matters Chapter 3 - K Nearest Neighbours Chapter 4 - K Means Clustering Chapter 5 - Naive Bayes Classifier Chapter 6 - Regression Methods Chapter 7 - Support Vector Machines Chapter 8 - Self-Organizing Maps Chapter 9 - Decision Tree Chapter 10 - Neural Networks Chapter 11 - Reinforcement Learning Datasets Epilogue Index
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