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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 LearningThe 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."

It's never too early to start learning about finance. But the mathematics covered in this book is not the mathematics of balancing your check book, or splitting a restaurant bill among friends, some of whom had the set menu, and others the specials. The mathematics here is an introduction to the subject known as mathematical finance, quantitative finance or financial engineering. It's an introduction to the higher level of mathematics used in banks and hedge funds, in valuing complex financial instruments, in allocating money to different assets, and in measuring and managing risk.This book is for high-school children who are confident in their mathematics. They do not have to be advanced or even top of their class. But they do have to be comfortable with learning new concepts.The book starts with a chapter on the history of money, banks, bubbles and crashes. Then there's the jargon. Bankers do like their jargon, but it's all very straightforward. Then there's a chapter on careers in finance. There are jobs that just require familiarity with spreadsheets, then there are jobs that require a degree in mathematics or computer science. A chapter on the basic mathematics is next. That's a little bit, but only a little bit, about the exponential function, logarithms and simple ideas in probability. The last four chapters are on fixed income (that's just putting money in the bank), shares, portfolios, and finally the financial instruments known as options.

This is the book that introduces schoolchildren to the mathematics behind Artificial Intelligence!There are books with no mathematics, just the description of what AI can do. Then there are books with no mathematics, just lots of computer code. And there are books with lots and lots of mathematics which are incomprehensible without a degree in mathematics.But there aren't books like this one!This book is for children who are confident in their mathematics. They do not have to be advanced or even top of their class. But they do have to be comfortable with learning new concepts. Here are some of those concepts, and how simple they are.Representing data as a vector: A list of numbers, don't be fooled by the big parentheses!Scaling the data: Taking away and multiplying. That was easy!Distance measurement: Taking away and adding up. Squaring and square rooting if you are feeling really adventurous!Charts: Putting dots on a piece of graph paper! Exhausted, not!The equation of a straight line: y = ax+b. You can do it!Averages: Adding up and dividing. You know how to divide, don't you, Steve?Yes, it's mathematics, but nothing to frighten the horses!The book starts with a chapter with some history of AI. This is followed by a chapter of jargon, useful mathematics, and a description of the main techniques. Then it's one chapter on each of Nearest Neighbours, Regression, Clustering, Decision Trees, Neural Networks and Reinforcement Learning. Each of these in-depth chapters covers uses of the technique, the mathematical details, a real example and then a project for readers to work through on their own or in teams. The book ends with sources for some useful data.