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Table of Contents
Prologue to 2nd Edition
CHAPTER 1: Introduction
CHAPTER 2: Preliminaries
CHAPTER 3: Neurons, Neural Networks, and Linear Discriminants
CHAPTER 4: The Multi-layer Perceptron
CHAPTER 5: Radial Basis Functions and Splines
CHAPTER 6: Dimensionality Reduction
CHAPTER 7: Probabilistic Learning
CHAPTER 8: Support Vector Machines
CHAPTER 9: Optimisation and Search
CHAPTER 10: Evolutionary Learning
CHAPTER 11: Reinforcement Learning
CHAPTER 12: Learning with Trees
CHAPTER 13: Decision by Committee: Ensemble Learning
CHAPTER 14: Unsupervised Learning
CHAPTER 15: Markov Chain Monte Carlo (MCMC) Methods
CHAPTER 16: Graphical Models
CHAPTER 17: Symmetric Weights and Deep Belief Networks
CHAPTER 18: Gaussian Processes
Summary
Machine Learning: An Algorithmic Perspective, Second Edition helps you understand the algorithms of machine learning. It puts you on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. This book covers a wide range of topics in machine learning, including neural networks, linear discriminants, support vector machines, evolutionary learning, reinforcement learning, decision trees, ensemble learning, unsupervised learning, Markov Chain Monte Carlo methods, graphical models, deep belief networks, and Gaussian processes. The text includes detailed examples, code implementations, and practical advice for applying machine learning algorithms.