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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Alex Giryavets (verified owner) –
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.
Steve S. (verified owner) –
Great read and good overview.
Vidal John Sisneros (verified owner) –
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 (verified owner) –
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.
mark (verified owner) –
no comment
Po the panda (verified owner) –
This was my subway read last month. Not too technical, mostly focuses on the intuition. Liked it.
Abby V (verified owner) –
Exactly what I anticipated.
ThinkTodd (verified owner) –
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!
Caleb (verified owner) –
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.
nascanio (verified owner) –
Book in good condition, excellent price and contents.