## Machine Learning: An Algorithmic PerspectiveTraditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text. Theory Backed up by Practical Examples The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve. Highlights a Range of Disciplines and Applications Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge. |

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### Contents

Introduction | 1 |

Linear Discriminants | 17 |

The MultiLayer Perceptron | 47 |

Radial Basis Functions and Splines | 95 |

Support Vector Machines | 119 |

Learning with Trees | 133 |

Decision by Committee Ensemble Learning | 153 |

Probability and Learning | 167 |

Dimensionality Reduction | 221 |

Optimisation and Search | 247 |

Evolutionary Learning | 269 |

Reinforcement Learning | 293 |

Markov Chain Monte Carlo MCMC Methods | 315 |

Graphical Models | 333 |

Python | 365 |

383 | |