Machine Learning

Paul Wilmott studied mathematics at St Catherine's College, Oxford, where he also received his D.Phil. He is the author of Paul Wilmott Introduces Quantitative Finance (Wiley 2007), Paul Wilmott On Quantitative Finance (Wiley 2006), Frequently Asked Questions in Quantitative Finance (Wiley 2009), The Money Formula (with David Orrell) (Wiley 2017) and other financial textbooks. He has written over 100 research articles on finance and mathematics. Paul Wilmott was a founding partner of the volatility arbitrage hedge fund Caissa Capital which managed $170 million. His responsibilities included forecasting, derivatives pricing, and risk management. Paul is the proprietor of wilmott.com, the popular quantitative finance community website, and the quant magazine Wilmott. He is the creator of the Certificate in Quantitative Finance, cqf.com, and the President of the CQF Institute, cqfinstitute.org.Paul Wilmott has been called "cult derivatives lecturer" by the Financial Times and "financial mathematics guru" by the BBC.

Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topicsK Nearest NeighboursK Means ClusteringNaïve Bayes ClassifierRegression MethodsSupport Vector MachinesSelf-Organizing MapsDecision TreesNeural NetworksReinforcement LearningThe book includes many real-world examples from a variety of fields includingfinance (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."