Bishop and Winn wrote:In this book we look at machine learning from a fresh perspective which we call model-based machine learning. This viewpoint helps to address all of these challenges, and makes the process of creating effective machine learning solutions much more systematic. It is applicable to the full spectrum of machine learning techniques and application domains, and will help guide you towards building successful machine learning solutions without requiring that you master the huge literature on machine learning.

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Who is this book for?

This book is rather unusual for a machine learning text book in that we do not review dozens of different algorithms. Instead we introduce all of the key ideas through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter therefore introduces one case study which is drawn from a real-world application that has been solved using a model-based approach. The exception is the first chapter which explores a simple fictional problem involving a murder mystery.

Each chapter also serves to introduce a variety of machine learning concepts, not as abstract ideas, but as concrete techniques motivated by the needs of the application. You can think of these concepts as the building blocks for constructing models. Although you will need to invest some time to understand these concepts fully, you will soon discover that a huge variety of models can be constructed from a relatively small number of building blocks. By working through the case studies in this book you will learn how to use these components, and will hopefully gain a sufficient appreciation of the power and flexibility of model-based approach to allow you to solve your machine learning problem.