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.
Alberto Santangelo –
Very nice introduction to machine learning, applied mathematicians will appreciate it. And one of the few books on machine learning (maybe the only one) to suggest the use of Matthews Correlation as a measure of performance of a classifier.
Alex Giryavets –
Great book on intuition behind broad spectrum of Machine Learning approaches, full of practical examples. In fact, it is the only book aside from the Elements of Statistical Learning that I would recommend (and own). It is in strike contrast to the plethora of ML books on the market that are either too math heavy with little practical examples, or just show you how to apply python or R packages.
Finally, entertaining value of this book should not be overlooked, not P. G. Wodehouse but close.
Everland Fennell –
Useful book
M –
Focused introduction to ML. Theory backed by examples which give necessary overview in this field. Easy and most important entertaining read in Paul Wilmott style.
Steve S. –
Great read and good overview.
Vidal John Sisneros –
Great resource. It’s like talking to someone one who is just giving you the simple straight answer to what’s going on. This book’s tone and depth is between the buzz word laden “intro to machine learning” books for business people and the “too much math for non majors” textbooks that focus a specific type of machine learning.
With that said I use it to gain an intuition and the first layers of mathematical depth to each ML algorithm. I believe that this does not replace a textbook but more of a straightforward companion. Highly recommend.
Plano shopper –
When I started out, I ran several trading desks on the financial futures floors at the CME and CBOT. Fundamental and technical analysis were all that existed. I found that the only way to learn the quantitative aspect of the markets (circa 1983) was by walking around the exchange floors right after the close, picking up research/strategy papers off the floor near the most quantitatively-oriented firms. Fortunately for us, books authored by Dr. Wilmott and others like him have shed a light into the math, minds, and methodology of one of the most interesting areas of global markets.
alb3rtazzo –
i have studied this book.
Paul Willmott is probably the best teacher i have ever had in my life. (cqf london )
he really knows how to make complex subjects very clear and easy to understand.
Matthias –
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.
Alexander Nadjalin –
For me, this book fills several gaps in my understanding of machine learning (ML) topics. It is my introduction to ML concepts, methods, definitions, jargon etc. What’s more important, and why I give this book 5 stars, is that there is no programming code in it. This means that the book focuses not on learning programming tools (like many articles online) but on the actual subjects and the math which ML is based on.
The examples come from a wide variety of areas and problems, which encourages the reader to think broader on how to apply ML techniques. Dr. Wilmott has a lot of humor which makes the book an enjoyable and interesting read.
I consider this book a solid foundation, and most importantly an inspiration for me to start exploring ML properly. I most definitely recommend this book!
Mohd nafees –
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.
Qujung –
I was floored with how intuitive this text seems. Machine Learning is usually planted as this incredibly abstract, theoretical subject, but Paul Wilmott makes it accessible to the layman. In the coming revolution with ML methods being standard in the industry, I highly recommend.
mark –
no comment
Po the panda –
This was my subway read last month. Not too technical, mostly focuses on the intuition. Liked it.
Sam T. –
Overall, a very good book.
Abby V –
Exactly what I anticipated.
Ziad –
I strongly recommend this book for new students and starters because it has the most applied and least math. For those of you who are afraid or weak with the advanced mathematics, ML is not for you, but this book breaks that barrier and focuses on the applications and you can get up and running quickly. I strongly recommend it to be your first book on the subject.
ThinkTodd –
The author gives a very good review of machine learning in theory or from an algorithmic point of view. You don’t see a single line of code, but you will be very familiar with the concepts implemented in ML packages like Sci-kit learn. Actually, it’ll help to understand what’s done in Python. If Sci-kit learn package is a Python library, this book will help “to explain what the code is doing” (page 7). I think the people who knows ML well can learn a lot from this short book – it’s relevant and up to date. The writing style is straightforward and fun to read!
R. Garnica –
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.
Jasmine S –
This book adds a lot of clarity and structure to the still opaque science of machine learning. And Paul, although a youngster, applies a lot of sarcasm and cynicism in the book, which typically only we old guys have… An informative and entertaining read!
Medha –
Detailed explanation, analysis and insights.
Would say some prerequisite computer science concept knowledge is mandatory.
Caleb –
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.
areader –
This is an informal introduction to machine learning techniques and philosophy. It is an easy reading and inexpensive book
Anatoliy Swishchuk –
I like nicely written and designed book and its efficient delivery. I use it for my research needs.
Leo –
A good summary to kick start the learning of machine learning.
Also very useful for all learners from different degrees of knowledge.
nascanio –
Book in good condition, excellent price and contents.