Model-Based Machine Learning, Free Early Book Draft
Today thousands of scientists and engineers are applying machine learning to an extraordinarily broad range of domains. Equip yourself with machine learning skills in an all new way by reading this free ebook, by John Winn and Christopher Bishop (with Thomas Diethe).
“I am overwhelmed by the choice of machine learning methods and techniques. There’s too much to learn!”
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.
Quick Look : Features
What is model-based machine learning?
Model-based machine learning can be applied to pretty much any problem, and its general-purpose approach means you don’t need to learn a huge number of machine learning algorithms and techniques.
Over the last five decades, researchers have created literally thousands of machine learning algorithms.
Traditionally an engineer wanting to solve a problem using machine learning must choose one or more of these algorithms to try, or otherwise try to invent a new one.
In practice, their choice of algorithm may be constrained by those algorithms they happen to be familiar with, or by the availability of specific software, and may not be the best choice for their problem.
Instead of having to transform your problem to fit some standard algorithm, in model-based machine learning you design the algorithm precisely to fit your problem.
The core idea at the heart of model-based machine learning is that all the assumptions about the problem domain are made explicit in the form of a model.
Models versus algorithms
Let’s look more closely at the relationship between models and algorithms.
We can think of a standard machine learning algorithm as a monolithic box which takes in data and produces results. The algorithm must necessarily make assumptions since it is these assumptions that distinguish a particular algorithm from the thousands of others out there. However, in an algorithm those assumptions are implicit and opaque.
Now consider the model-based view. The model comprises the set of assumptions we are making about the problem domain. To get from the model to a set of predictions we need to take the data and compute those variables whose values we wish to know. This computational process we shall call inference. There are several techniques available for doing inference which will be discussed in the book.
Tools for model-based machine learning
All of the models in this book were created using Infer.NET (a software framework at Microsoft Research), and the corresponding model source code is available online.
Get the ebook at http://www.mbmlbook.com/Introduction.html