Machine Learning: An Applied Mathematics Introduction

✍ Scribed by Paul Wilmott

• Publisher: Panda Ohana Publishing
• Year: 2019
• Tongue: English
• Leaves: 246
• Category: Library

✦ 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, 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.

✦ Table of Contents

Contents
Prologue
Chapter 1 - Introduction
Chapter 2 - General Matters
Chapter 3 - K Nearest Neighbours
Chapter 4 - K Means Clustering
Chapter 5 - Naive Bayes Classifier
Chapter 6 - Regression Methods
Chapter 7 - Support Vector Machines
Chapter 8 - Self-Organizing Maps
Chapter 9 - Decision Tree
Chapter 10 - Neural Networks
Chapter 11 - Reinforcement Learning
Datasets
Epilogue
Index