Machine learning is a broad and fascinating field. Even
today, machine learning technology runs a substantial part of your
life, often without you knowing it. Any plausible approach to artifi-
cial intelligence must involve learning, at some level, if for no other
reason than it’s hard to call a system intelligent if it cannot learn.
Machine learning is also fascinating in its own right for the philo-
sophical questions it raises about what it means to learn and succeed
at tasks.
The past decade has seen an explosion of machine learning research and appli-
cations; especially, deep learning methods have enabled key advances in many
applicationdomains,suchas computervision,speechprocessing,andgameplaying.
However, the performance of many machine learning methods is very sensitive
to a plethora of design decisions, which constitutes a considerable barrier for
new users. This is particularly true in the booming field of deep learning, where
human engineers need to select the right neural architectures, training procedures,
regularization methods, and hyperparameters of all of these components in order to
make their networks do what they are supposed to do with sufficient performance.
This process has to be repeated for every application. Even experts are often left
with tedious episodes of trial and error until they identify a good set of choices for
a particular dataset.
Pattern recognition has its origins in engineering, whereas machine learning grew
out of computer science. However, these activities can be viewed as two facets of
the same field, and together they have undergone substantial development over the
past ten years. In particular, Bayesian methods have grown from a specialist niche to
become mainstream, while graphical models have emerged as a general framework
for describing and applying probabilistic models. Also, the practical applicability of
Bayesian methods has been greatly enhanced through the development of a range of
approximate inference algorithms such as variational Bayes and expectation propa-
gation. Similarly, new models based on kernels have had significant impact on both
algorithms and applications.
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.
