Did you know you can trade in your old books for an Amazon.co.uk Gift Card to spend on the things you want? Plus, get an extra £5 Gift Certificate when you trade in books worth £10 or more before June 30, 2012. Visit the Books Trade-In Store for more details. |
Frequently Bought Together
Product details
|
More About the Author
Product Description
Review
"This volume offers a very accessible introduction to the field of machine learning. Ethem Alpaydin gives a comprehensive exposition of the kinds of modeling and prediction problems addressed by machine learning, as well as an overview of the most common families of paradigms, algorithms, and techniques in the field. The volume will be particularly useful to the newcomer eager to quickly get a grasp of the elements that compose this relatively new and rapidly evolving field." --Joaquin Quinonero-Candela, coeditor, Dataset Shift in Machine Learning
Review
"A few years ago, I used the first edition of this book as a reference book for a project I was working on. The clarity of the writing, as well as the excellent structure and scope, impressed me. I am more than pleased to find that this second edition continues to be highly informative and comprehensive, as well as easy to read and follow." Radu State Computing Reviews
Inside This Book
(Learn More)
Tags Customers Associate with This Product(What's this?)Click on a tag to find related items, discussions, and people.
|
Sell a Digital Version of This Book in the Kindle Store
If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store.
Learn more
What Other Items Do Customers Buy After Viewing This Item?
Customer Reviews
There are no customer reviews yet on Amazon.co.uk.
5 star
4 star
3 star
2 star
1 star
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
7 reviews
6 of 6 people found the following review helpful
Great book for Learning Machine Learning
16 Oct 2011
Format:Hardcover|Amazon Verified Purchase
This book is perfect for both the self-learners that like to learn from scratch and for the ones who need to know crucial details of a method in order to use it as a tool. Compared to 'Pattern Classification by Duda, Hart, and Stork', this book has a good balance between providing equations and explaining the idea behind the method. One thing that I like is that the author usually derives the equations. For example, I used the book to implement Hidden Markov Models algorithm in Java for classification. Especially, if you need a good source to learn Support Vector Machines, 'Chapter 10 Linear Discrimination' and 'Chapter 13 Kernel Machines' are the best of their kinds in the Machine Learning literature. Furthermore, examples shown in the figures are unique and very helpful to understand the topic. The author covers some methods that you usually see in the papers but not in the textbooks. Therefore, the book is also a good survey of Machine Learning techniques. In a nutshell, a great resource for those who want to use Machine Learning Algorithms for classification or regression as a tool and for those who want to implement Machine Learning Algorithms in their applications.
5 of 5 people found the following review helpful
Good for beginner
20 Oct 2011
Format:Hardcover
Easy to understand and covers most topic in ML. If you are an intro level student in ML or self studier in ML, this book is best.
1 of 1 people found the following review helpful
A good introduction with a few minor issues
16 Mar 2012
Format:Hardcover
This semester I am taking a class on statistical learning theory where we prove bounds on various learning algorithms and I came to realize that I did not know all of the methods that we were proving bounds on. To bring myself up to speed, I picked up this book. Having had only minimal exposure to the algorithms that underlie machine learning, I found this introduction to be very useful. It starts with a concise, but by no means terse review of basic statistics which lays the foundation for the rest of the book. If you struggle to get through this review, or if it is new material, this may not be the book for you. I should say that the author does not shy away from using equations, but does not use them gratuitously either. He also does a reasonable job of not only explaining the steps that may not be intuitive as well as giving some motivation for what the equations actually mean.
After reading this book, I can actually say that I have a much deeper understanding of many the algorithms discussed. I found the exposition on principle component analysis (PCA) to be very enlightening (I have come across PCA in my work and had not previously found an explanation that I could understand) and the whole chapter on dimensionality reduction fascinating. The chapters that discuss clustering and kernel methods were also good. Also, the way that each chapter, which roughly corresponds to a single method, first focuses on the way the algorithm can be used for classification and then the more general case of regression was well thought out.
This book does have some drawbacks though. For instance, there are many careless typos in some chapters, making you wonder if they just forgot to proofread these chapters. Even more infuriating, I am fairly certain that I came across at least one equation that was misprinted. After just one wrong equation, you start to question the veracity of every one which you do not fully comprehend. Also, I must say that I still only vaguely understand how a multilayer percetron works even though it is a major focus of the book. Also, the chapter on Bayesian estimation was hard to follow.
All in all, I think this book is well worth the price and that if you devote the time needed to read it you will learn a lot.
After reading this book, I can actually say that I have a much deeper understanding of many the algorithms discussed. I found the exposition on principle component analysis (PCA) to be very enlightening (I have come across PCA in my work and had not previously found an explanation that I could understand) and the whole chapter on dimensionality reduction fascinating. The chapters that discuss clustering and kernel methods were also good. Also, the way that each chapter, which roughly corresponds to a single method, first focuses on the way the algorithm can be used for classification and then the more general case of regression was well thought out.
This book does have some drawbacks though. For instance, there are many careless typos in some chapters, making you wonder if they just forgot to proofread these chapters. Even more infuriating, I am fairly certain that I came across at least one equation that was misprinted. After just one wrong equation, you start to question the veracity of every one which you do not fully comprehend. Also, I must say that I still only vaguely understand how a multilayer percetron works even though it is a major focus of the book. Also, the chapter on Bayesian estimation was hard to follow.
All in all, I think this book is well worth the price and that if you devote the time needed to read it you will learn a lot.
Were these reviews helpful?
Let us know
Listmania!
Look for similar items by category
Look for similar items by subject
Feedback
- Would you like to update product info or give feedback on images?
- I am the Author, and I want to comment on my book.
- I am the Publisher, and I want to comment on this book.
|
