[Early Access Version] Model-Based Machine Learning

[Early Access Version] Model-Based Machine Learning

This book looks at machine learning from a perspective called model-based machine learning. This viewpoint will guide you towards building successful machine learning solutions without requiring that you master the huge literature on machine learning.

Publication date: 01 Jan 2016

ISBN-10: n/a

ISBN-13: n/a

Paperback: n/a

Views: 2,754

Type: Book

Publisher: n/a

License: n/a

Post time: 15 Dec 2016 08:00:00

[Early Access Version] Model-Based Machine Learning

[Early Access Version] Model-Based Machine Learning This book looks at machine learning from a perspective called model-based machine learning. This viewpoint will guide you towards building successful machine learning solutions without requiring that you master the huge literature on machine learning.
Tag(s): Machine Learning
Publication date: 01 Jan 2016
ISBN-10: n/a
ISBN-13: n/a
Paperback: n/a
Views: 2,754
Document Type: Book
Publisher: n/a
License: n/a
Post time: 15 Dec 2016 08:00:00
Admin's Note:

This is an early access version of a book in progress. There is no guarantee this book will remain freely accessible once it is completed and published. I will deactivate this post if this book is no longer freely available.

From the Introduction:
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.

...

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.




About The Author(s)


Chris Bishop is a Microsoft Distinguished Scientist and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, and in 2007 he was elected Fellow of the Royal Society of Edinburgh. Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory.

Christopher Bishop

Chris Bishop is a Microsoft Distinguished Scientist and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, and in 2007 he was elected Fellow of the Royal Society of Edinburgh. Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory.


John Winn is a Principal Researcher at Microsoft Research, Cambridge, in the Machine Learning and Perception group. His main research interests are machine learning, machine vision and computational biology. Previously, he was a Ph.D. student in the Inference Group at the Cavendish Laboratory, supervised by Chris Bishop and David MacKay. He has also been a member of the Signal Processing Group at the Engineering Department and the Learning and Vision Group at the MIT AI Lab

John Winn

John Winn is a Principal Researcher at Microsoft Research, Cambridge, in the Machine Learning and Perception group. His main research interests are machine learning, machine vision and computational biology. Previously, he was a Ph.D. student in the Inference Group at the Cavendish Laboratory, supervised by Chris Bishop and David MacKay. He has also been a member of the Signal Processing Group at the Engineering Department and the Learning and Vision Group at the MIT AI Lab


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