Paul Wilmott Bücher

Maschinelles Lernen – alle Grundlagen! Paul Wilmott ist für seine erhellende und unterhaltsame Darstellung angewandter Mathematik bekannt. Von der linearen Regression bis zu Neuronalen Netzwerken führt er Sie durch alle Verfahren, und zwar komplett Software-unabhängig. Der Vorteil dabei: Jeder Schritt ist schwarz auf weiß zu sehen, kein Framework kann etwas „verstecken“, es geht immer um die Sache selbst. Mit vielen Beispielen, Grafiken und Schritt-für-Schritt-Kästen. Für alle, die wirklich verstehen wollen, wie Maschinen lernen. Aus dem Inhalt: Lineare Regression k-Nearest Neighbors Naive Bayes-Klassifikatoren k-Means-Algorithmus Support Vector Machines Logistische Regression Selbstorganisierende Karten Entscheidungsbäume Reinforcement Learning Neuronale Netze

Explore the deadly elegance of finance's hidden powerhouse The Money Formula takes you inside the engine room of the global economy to explore the little- understood world of quantitative finance, and show how the future of our economy rests on the backs of this all-but-impenetrable industry.

The new edition of this finance classic serves as a comprehensive reference on both traditional and new derivatives and financial engineering techniques. Explaining finance in an accessible manner, Wilmott covers all the current financial theories in quantitative finance and makes them easy to understand and implement.

Paul Wilmott Introduces Quantitative Finance, Second Edition offers a clear introduction to classical quantitative finance tailored for university students.

Getting agreement between finance theory and finance practice is important like never before. In the last decade the derivatives business has grown to a staggering size, such that the outstanding notional of all contracts is now many multiples of the underlying world economy. No longer are derivatives for helping people control and manage their financial risks from other business and industries, no, it seems that the people are toiling away in the fields to keep the derivatives market afloat! (Apologies for the mixed metaphor!) If you work in derivatives, risk, development, trading, etc. you'd better know what you are doing, there's now a big responsibility on your shoulders. In this second edition of Frequently Asked Questions in Quantitative Finance I continue in my mission to pull quant finance up from the dumbed-down depths, and to drag it back down to earth from the super-sophisticated stratosphere. Readers of my work and blogs will know that I think both extremes are dangerous. Quant finance should inhabit the middle ground, the mathematics sweet spot, where the models are robust and understandable, and easy to mend. ...And that's what this book is about. This book contains important FAQs and answers that cover both theory and practice. There are sections on how to derive Black-Scholes (a dozen different ways!), the popular models, equations, formulae and probability distributions, critical essays, brainteasers, and the commonest quant mistakes. The quant mistakes section alone is worth trillions of dollars! I hope you enjoy this book, and that it shows you how interesting this important subject can be. And I hope you'll join me and others in this industry on the discussion forum on wilmott.com. See you there!” FAQQF2...including key models, important formulae, popular contracts, essays and opinions, a history of quantitative finance, sundry lists, the commonest mistakes in quant finance, brainteasers, plenty of straight-talking, the Modellers' Manifesto and lots more.

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

Finance is one of the fastest growing areas in the modern banking and corporate world. This, together with the sophistication of modern financial products, provides a rapidly growing impetus for new mathematical models and modern mathematical methods. Indeed, the area is an expanding source for novel and relevant "real-world" mathematics. In this book, the authors describe the modeling of financial derivative products from an applied mathematician's viewpoint, from modeling to analysis to elementary computation. The authors present a unified approach to modeling derivative products as partial differential equations, using numerical solutions where appropriate. The authors assume some mathematical background, but provide clear explanations for material beyond elementary calculus, probability, and algebra. This volume will become the standard introduction for advanced undergraduate students to this exciting new field.

The book offers an introduction to mathematical finance, focusing on advanced mathematical concepts used in banks and hedge funds. It covers essential topics such as valuing complex financial instruments, asset allocation, and risk management. Unlike basic financial math, this text delves into the quantitative methods that underpin modern finance, making it suitable for those interested in the intricacies of financial engineering and the mathematical principles that drive the industry.