Description
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.
A good summary to kick start the learning of machine learning.
Also very useful for all learners from different degrees of knowledge.
I like nicely written and designed book and its efficient delivery. I use it for my research needs.
This is an informal introduction to machine learning techniques and philosophy. It is an easy reading and inexpensive book
Coming from a non mathematical backround the explanation of each algorithm and idea presented in the book was very easy to grasp (although I got lost when trying to follow the more advanced equations). This book has really improved my understanding on the topic and I am giving it a second read to fully understand all the math. This book is a good choice for a layman who wants to dive head first into the topic and doesn’t mind having some of the mathematical principles goes over their head the first run through.
Detailed explanation, analysis and insights.
Would say some prerequisite computer science concept knowledge is mandatory.