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.”

1. Introduction 2. General Matters 3. K Nearest Neighbours 4. K Means Clustering 5. Naive Bayes Classifier 6. Regression Methods 7. Support Vector Machines 8. Self-Organizing Maps 9. Decision Trees 10. Neural Networks 11. Reinforcement Learning Datasets Epilogue Index