Machine Learning: An Applied Mathematics Introduction

Wilmott, Paul

ISBN 10: 1916081606 / ISBN 13: 9781916081604

Edité par Panda Ohana Publishing, 2019

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Synopsis :

A fully self-contained introduction to machine learning. All that the reader requires is an understanding of the basics of matrix algebra and calculus. Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the most important techniques.

Chapter list:

1. Introduction (Putting ML into context. Comparing and contrasting with classical mathematical and statistical modelling)
2. General Matters (In one chapter all of the mathematical concepts you'll need to know. From jargon and notation to maximum likelihood, from information theory and entropy to bias and variance, from cost functions to confusion matrices, and more)
3. K Nearest Neighbours
4. K Means Clustering
5. Naïve Bayes Classifier
6. Regression Methods
7. Support Vector Machines
8. Self-Organizing Maps
9. Decision Trees
10. Neural Networks
11. Reinforcement Learning

An appendix contains links to data used in the book, and more.

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)
• business (using CEO speeches to determine stock price movement)
• 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)
• marketing (classifying the members of his own website to see who will subscribe to his magazine)

Paul Wilmott brings three decades of experience in education, and his inimitable style, to this, the hottest of subjects. This book is an accessible introduction for anyone who wants to understand the foundations and put the tools into practice.

Paul Wilmott has been called “cult derivatives lecturer” by the Financial Times and “financial mathematics guru” by the BBC.

À propos de l?auteur: 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 www.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.

Détails bibliographiques

Titre : Machine Learning: An Applied Mathematics ...
Éditeur : Panda Ohana Publishing
Date d'édition : 2019
Reliure : Paperback
Etat : Good