Machine Learning: An Applied Mathematics Introduction

Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics



K Nearest Neighbours



K Means Clustering



Naïve Bayes Classifier



Regression Methods



Support Vector Machines



Self-Organizing Maps



Decision Trees



Neural Networks



Reinforcement Learning











The book includes many real-world examples from a variety of fields including





finance (volatility modelling)



economics (interest rates, inflation and GDP)



politics (classifying politicians according to their voting records, and using speeches to determine whether a politician is left or right wing)



biology (recognising flower varieties, and using heights and weights of adults to determine gender)



sociology (classifying locations according to crime statistics)



gambling (fruit machines and Blackjack)



business (classifying the members of his own website to see who will subscribe to his magazine!)











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