ABSTRACT

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

chapter Chapter 1|14 pages

Introduction

chapter Chapter 2|24 pages

Preliminaries

chapter Chapter 3|32 pages

Neurons, Neural Networks, and Linear Discriminants

chapter Chapter 4|40 pages

The Multi-layer Perceptron

chapter Chapter 5|18 pages

Radial Basis Functions and Splines

chapter Chapter 6|24 pages

Dimensionality Reduction

chapter Chapter 7|16 pages

Probabilistic Learning

chapter Chapter 8|20 pages

Support Vector Machines

chapter 9|22 pages

Optimisation and Search

chapter 10|20 pages

Evolutionary Learning

chapter 11|18 pages

Reinforcement Learning

chapter 12|18 pages

Learning with Trees

chapter 13|14 pages

Decision by Committee: Ensemble Learning

chapter 14|24 pages

Unsupervised Learning

chapter Chapter 15|16 pages

Markov Chain Monte Carlo (MCMC) Methods

chapter Chapter 16|38 pages

Graphical Models

chapter Chapter 17|36 pages

Symmetric Weights and Deep Belief Networks

chapter 18|20 pages

Gaussian Processes