Machine Learning

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.

Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

New to the Second Edition

• Two new chapters on deep belief networks and Gaussian processes
• Reorganization of the chapters to make a more natural flow of content
• Revision of the support vector machine material, including a simple implementation for experiments
• New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
• Additional discussions of the Kalman and particle filters
• Improved code, including better use of naming conventions in Python

Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.

Table of Contents

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 DeepBelief Networks

Chapter 18: Gaussian Processes

APPENDIX A: Python

About the Author

Stephen Marsland is a professor of scientific computing and the postgraduate director of the School of Engineering and Advanced Technology (SEAT) at Massey University. His research interests in mathematical computing include shape spaces, Euler equations, machine learning, and algorithms. He received a PhD from Manchester University