<p><em>Machine Learning: An Applied Mathematics Introduction</em>&nbsp;covers the essential mathematics behind all of the following topics</p>
<ul>
<li><em>K</em>&nbsp;Nearest Neighbours&nbsp;</li>
<li><em>K</em>&nbsp;Means Clustering</li>
<li>Na&iuml;ve Bayes Classifier</li>
<li>Regression Methods</li>
<li>Support Vector Machines</li>
<li>Self-Organizing Maps</li>
<li>Decision Trees</li>
<li>Neural Networks</li>
<li>Reinforcement Learning</li>
</ul>

<p>The book includes many real-world examples from a variety of fields including</p>

<ul>
<li>finance (volatility modelling)</li>
<li>economics (interest rates, inflation and GDP)</li>
<li>politics (classifying politicians according to their voting records, and using speeches to determine whether a politician is left or right wing)</li>
<li>biology (recognising flower varieties, and using heights and weights of adults&nbsp;to determine gender)</li>
<li>sociology (classifying locations according to crime statistics)</li>
<li>gambling (fruit machines and Blackjack)</li>
<li>business (classifying the members of his own website to see who will subscribe to his magazine!)</li>
</ul>

<p>Paul Wilmott brings three decades of experience in mathematics education, and his&nbsp;inimitable&nbsp;style, to&nbsp;the hottest of subjects. This book is an accessible introduction for anyone who wants to understand the foundations but also wants to &ldquo;get to the meat without having to eat too many vegetables.&rdquo;</p>