ABSTRACT

Traditional 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

chapter 1|16 pages

Introduction

chapter 2|30 pages

Linear Discriminants

chapter 3|48 pages

The Multi-Layer Perceptron

chapter 4|24 pages

Radial Basis Functions and Splines

chapter 5|14 pages

Support Vector Machines

chapter 6|20 pages

Learning with Trees

chapter 7|14 pages

Decision by Committee: Ensemble Learning

chapter 8|28 pages

Probability and Learning

chapter 9|26 pages

Unsupervised Learning

chapter 10|26 pages

Dimensionality Reduction

chapter 11|22 pages

Optimisation and Search

chapter 12|24 pages

Evolutionary Learning

chapter 13|22 pages

Reinforcement Learning

chapter 14|18 pages

Markov Chain Monte Carlo (MCMC) Methods

chapter 15|32 pages

Graphical Models

chapter 16|18 pages

Python