Machine Learning: An Applied Mathematics Introduction
Original price was: $70.00.$17.00Current price is: $17.00.
PDF 16,73 MB • Pages: 246
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)
- 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.
17 reviews for Machine Learning: An Applied Mathematics Introduction
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Everland Fennell (verified owner) –
Useful book
Matthias (verified owner) –
Gutes Buch von etablierten Autor, günstiger Preis. Für meinen Geschmack etwas zu elementar, ein bisschen mehr Tiefe aufgrund von ‘Mathematics’ in Titel wäre zu erwarten gewesen.
Mohd nafees (verified owner) –
The book I received is quite new and looks authentic. Though it was a bit damage from corner but I am satisfied with the product.seller looks honest as it sell original products.
Sam T. (verified owner) –
Overall, a very good book.
R. Garnica (verified owner) –
Due to not taking the book’s title at face value, I didn’t realize what I was getting into. I misappropriated the “introduction” term and as such, it left me disappointed with the book. This is not the book’s fault however.
This is the type of book that’s useful if you have a strong foundation in math. There’s it’s subtitle is “…An Applied Mathematics Introduction.”
It is such that if you do have strong math skills, then this book will be of great importance to you as you understand how to apply math towards machine learning.
If you are like me and learning machine learning on your own and don’t quite have the mathematical foundation then it will be a high hurdle to overcome as you read.
The book is broken down into chapters which cover various machine learning methodologies. They give a quick synopsis of what the methodology is and when you would use. Then the math begins.
It is by no means heavy on math, but it is rich in math. I do enjoy Wilmott’s writing style but for me, I need a more basic introduction towards math and due to my own misreading of the title, thought this would aid me.
For a pop culture analogy: there’s a Simpsons episode where Homer is reading a book on advanced marketing. He doesn’t grasp it and in the next scene he is reading a book on beginning marketing. He doesn’t grasp that either and then reads the definition of marketing in a dictionary. For me, this book is like the advanced marketing book when I need to learn the definitions first.
Medha (verified owner) –
Detailed explanation, analysis and insights.
Would say some prerequisite computer science concept knowledge is mandatory.
areader (verified owner) –
This is an informal introduction to machine learning techniques and philosophy. It is an easy reading and inexpensive book