This book is a general introduction to machine learning that can serve as a reference
book for researchers and a textbook for students. It covers fundamental modern
topics in machine learning while providing the theoretical basis and conceptual tools
needed for the discussion and justification of algorithms. It also describes several
key aspects of the application of these algorithms.
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers
usually do not explain their predictions which is a barrier to the adoption of machine learning.
This book is about making machine learning models and their decisions interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable models
such as decision trees, decision rules and linear regression. Later chapters focus on general model-
agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal
effects and explaining individual predictions with Shapley values and LIME.
Much has been written concerning the manner in which healthcare is changing, with
a particular emphasis on how very large quantities of data are now being routinely
collected during the routine care of patients. The use of machine learning meth-
ods to turn these ever-growing quantities of data into interventions that can improve
patient outcomes seems as if it should be an obvious path to take. However, the
field of machine learning in healthcare is still in its infancy. This book, kindly
supported by the Institution of Engineering andTechnology, aims to provide a “snap-
shot” of the state of current research at the interface between machine learning and
healthcare.
Machine learning is about designing algorithms that automatically extract
valuable information from data. The emphasis here is on “automatic”, i.e.,
machine learning is concerned about general-purpose methodologies that
can be applied to many datasets, while producing something that is mean-
ingful. There are three concepts that are at the core of machine learning:
data, a model, and learning.
Recent advances in experimental methods have resulted in the generation
of enormous volumes of data across the life sciences. Hence clustering and
classification techniques that were once predominantly the domain of ecologists
are now being used more widely. This book provides an overview of these
important data analysis methods, from long-established statistical methods
to more recent machine learning techniques. It aims to provide a framework
that will enable the reader to recognise the assumptions and constraints that
are implicit in all such techniques. Important generic issues are discussed first
and then the major families of algorithms are described. Throughout the focus
is on explanation and understanding and readers are directed to other resources
that provide additional mathematical rigour when it is required. Examples
taken from across the whole of biology, including bioinformatics, are provided
throughout the book to illustrate the key concepts and each technique’s
potential.
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
The present era of research and development is all about interdisciplinary studies
attempting to better comprehend and model our understanding of this vast universe.
The fields of biology and computer science are no exception. This book discusses
some of the innumerable ways in which computational methods can be used to
facilitate research in biology and medicine—from storing enormous amounts of
biological data to solving complex biological problems and enhancing the treatment
of various diseases.
a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications.
ApMl provides users with the ability to crawl the web and download pages to their computer in a directory structure suitable for a Machine Learning system to both train itself and classify new documents. Classification Algorithms include Naive Bayes, KNN
