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Machine Learning: An Applied Mathematics Introduction
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:
Introduction (Putting ML into context. Comparing and contrasting with classical mathematical and statistical modelling)
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)
K Nearest Neighbours
K Means Clustering
Naïve Bayes Classifier
Regression Methods
Support Vector Machines
Self-Organizing Maps
Decision Trees
Neural Networks
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, 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 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.
Chapter list:
Introduction (Putting ML into context. Comparing and contrasting with classical mathematical and statistical modelling)
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)
K Nearest Neighbours
K Means Clustering
Naïve Bayes Classifier
Regression Methods
Support Vector Machines
Self-Organizing Maps
Decision Trees
Neural Networks
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, 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 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.
242 pages, Paperback
Published May 26, 2019
18 people are currently reading
205 people want to read
About the author
Paul Wilmott
36 books83 followersPaul Wilmott is a researcher, consultant and lecturer in quantitative finance. He is best known as the author of various academic and practitioner texts on risk and derivatives, and for Wilmott magazine and Wilmott.com , a quantitative finance portal.
He is the co-owner and Course Director for the Certificate in Quantitative Finance (CQF), a half year distance learning course on mathematical finance at 7City Learning, a London-based company providing training for the financial services industry. He is a founding partner of Caissa Capital, a volatility arbitrage hedge fund. He is on the editorial board of the academic journal International Journal of Theoretical and Applied Finance. He founded the Diploma in Mathematical Finance at Oxford University and the journal Applied Mathematical Finance. He is a director of Wilmott Electronic Media, which manages Wilmott.com, a website for the quantitative analyst community, and is a director of Paul & Dominic Quant Recruitment.
He studied mathematics at St Catherine’s College, Oxford University, where he received his D.Phil in Applied mathematics in 1985.
He is the co-owner and Course Director for the Certificate in Quantitative Finance (CQF), a half year distance learning course on mathematical finance at 7City Learning, a London-based company providing training for the financial services industry. He is a founding partner of Caissa Capital, a volatility arbitrage hedge fund. He is on the editorial board of the academic journal International Journal of Theoretical and Applied Finance. He founded the Diploma in Mathematical Finance at Oxford University and the journal Applied Mathematical Finance. He is a director of Wilmott Electronic Media, which manages Wilmott.com, a website for the quantitative analyst community, and is a director of Paul & Dominic Quant Recruitment.
He studied mathematics at St Catherine’s College, Oxford University, where he received his D.Phil in Applied mathematics in 1985.
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Displaying 1 - 7 of 7 reviews
November 16, 2019
Nice
This entire review has been hidden because of spoilers.
September 6, 2022
1. What is this?
The Book is a frank summary of Machine Learning, focusing on applied mathematics.
2. How is this work different than others?
Paul gives clear, crisp details of popular machine learning algorithms. This work is accessible.
3. Who should read it?
One who wants to learn, how to communicate, write clearly, understand algorithms.
Deus Vult,
Gottfried
The Book is a frank summary of Machine Learning, focusing on applied mathematics.
2. How is this work different than others?
Paul gives clear, crisp details of popular machine learning algorithms. This work is accessible.
3. Who should read it?
One who wants to learn, how to communicate, write clearly, understand algorithms.
Deus Vult,
Gottfried
October 20, 2023
Big thanks to the course instructor for assigning this book. It’s a really fun read — and the chapters are organised in terms of increasing difficulty
Plus, you can understand concepts without needing to wade into all that math. So you won’t feel guilty when the eyes start glaze over all the math notation…
Plus, you can understand concepts without needing to wade into all that math. So you won’t feel guilty when the eyes start glaze over all the math notation…
April 5, 2020
I liked the mathematical intuition but overall the book is very sloppy and full of mistakes as if written in a rush.
March 12, 2021
A good introduction to the topic, written in a very friendly manner. Good for beginners. But lacks depth.
July 12, 2020
A good and easy introductory text....
June 28, 2022
Good introduction to the subject, with explanations in plain english, enough math to make it serious and good references.
The examples could be more detailed and include some code.
The examples could be more detailed and include some code.
Displaying 1 - 7 of 7 reviews