The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
 |
| ||||||||||||||||||
Save up to 70% when you rent your textbooks on Amazon. Keep your textbook rentals for a semester and rental return shipping is free. |
Book Description
Frequently Bought Together
Editorial Reviews
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
About the Author
Ethem Alpaydin is Professor in the Department of Computer Engineering at Bogaziçi University, Istanbul.
Product Details
Would you like to update product info or give feedback on images? |
More About the Author
Ethem ALPAYDIN received his BSc from the Department of Computer Engineering of Boğaziçi University in 1987 and the degree of Docteur es Sciences from Ecole Polytechnique Fédérale de Lausanne in 1990. He did his postdoctoral work at the International Computer Science Institute, Berkeley in 1991 and afterwards was appointed Assistant Professor at the Department of Computer Engineering of Bogazici University. He became Associate Professor in 1996 and Professor in 2002 in the same department. As visiting researcher, he worked at the Department of Brain and Cognitive Sciences of MIT in 1994, the International Computer Science Institute, Berkeley in 1997 and IDIAP, Switzerland in 1998. He was a Fulbright Senior Scholar in 1997/1998 and received the Research Excellence Award from the Bogazici University Foundation in 1998 and 2008, the Young Scientist Award from the Turkish Academy of Sciences in 2001 and the Scientific Encouragement Award from the Turkish Scientific and Technical Research Council in 2002. His book Introduction to Machine Learning was published by The MIT Press in October 2004. Its German edition was published by Oldenbourg Verlag in May 2008, and its Chinese edition was published by Huazhang Press in June 2009. Introduction to Machine Learning, second edition was published by The MIT Press in February 2010 and its Turkish edition was published by Boğaziçi University Press in April 2011. He is a senior member of the IEEE, an editorial board member of The Computer Journal (Oxford University Press) and an associate editor of Pattern Recognition (Elsevier).
Customer Reviews
Share your thoughts with other customers
Most Helpful Customer Reviews
31 of 34 people found the following review helpful
Format:Hardcover|Amazon Verified Purchase
The topics and concepts in this book are exceptionally well organized. After reading it from cover to cover, I could easily see how all the ideas and concepts fit into place. I have two main criticisms. First, the notation is sometimes non-standard, e.g. the r vector is used to denote the label vector and superscripts are used sometimes as subscripts. Second, the explanations are sometimes too brief. For example, when deriving the solution for Least Squares Regression with Quadratic Discriminants, Vandermode matrices are used but the author fails to identify them as such, or to explain why they are useful. If the author were to write an extra sentence on every other page, the explanations would be perfect!
20 of 23 people found the following review helpful
By Subrat Nanda
Format:Hardcover
I would like to congratulate the author on writing this book, which is crisp and covers whole range of topics. What I liked the most is a systematic disucssion on a wide variety of areas in machine learning with a certain degree of details.
But at the same time, I will also say that the book at some places,(for eg the treatment of Multi Dimensional scaling and Linear discriminants analysis,) lacks depth in its derivations. Also if some explanatory examples are put,it would help the reader, who is doing a first time reading, in understanding the concepts.
At the same time, I think the book achieves it's target of introducing to the reader, a whole gamet of techniques, at a fairly reasonable level. The book is no doubt, a nice and one-stop quick reference for many topics, as such. A commendable thing is an up to date errata maintained by the author, with latest editions made. I would recommend the book for a quick introduction to the subject.
But at the same time, I will also say that the book at some places,(for eg the treatment of Multi Dimensional scaling and Linear discriminants analysis,) lacks depth in its derivations. Also if some explanatory examples are put,it would help the reader, who is doing a first time reading, in understanding the concepts.
At the same time, I think the book achieves it's target of introducing to the reader, a whole gamet of techniques, at a fairly reasonable level. The book is no doubt, a nice and one-stop quick reference for many topics, as such. A commendable thing is an up to date errata maintained by the author, with latest editions made. I would recommend the book for a quick introduction to the subject.
8 of 8 people found the following review helpful
By H. Haberdar
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.
Most Recent Customer Reviews
4.0 out of 5 stars
Primary reference book for machine learning techniques
This book meets all my need for a reference book in machine learning domain. Specifically, the second edition covers all fundamental topics. Read more
Published 1 month ago by Ahmet Hungari
3.0 out of 5 stars
hard to read
this is the text book required by our professor, however, the notation is horrible, difficult to understand. I prefer the mitchel book better
Published 1 month ago by Pei-Hsien Sun
5.0 out of 5 stars
Really great for beginners, but make sure you start at the beginning
This book is very easy to read, but make sure you start at the beginning. I was able to learn quite a bit about ML in just a month by reading about half of this book. Read more
Published 2 months ago by Hadayat Seddiqi
1.0 out of 5 stars
Argh tooo abstract, jargon and equations puzzle!
This book is meant for a highly educated audience, but educated in the AI and Machine Learning something. Read more
Published 3 months ago by Rafael Carvajal Díaz
3.0 out of 5 stars
Good book for a class.
According to my professor, it was one of the most accessible book for Machine Learning subject. I agree in the sense of the examples it contains. Read more
Published 11 months ago by Mathieu LP
4.0 out of 5 stars
A good introduction with a few minor issues
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... Read more
Published 14 months ago by Joe Jordan
5.0 out of 5 stars
Very nice and accessible way to learn Machine Learning
I am surprised to see so few reviews for this book. One reason could be that a lot of people are not aware of the existence of this book. Read more
Published 15 months ago by andy
5.0 out of 5 stars
Good for beginner
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.
Published 19 months ago by Emre Demir
2.0 out of 5 stars
Not useful as a textbook
Looks like a textbook, but it's really more like a handy reference for machine learning theory. Every time I thought the author was about to actually show how to implement an... Read more
Published 21 months ago by August Rauber
2.0 out of 5 stars
Light on detail and lacking solutions
Nice breadth of examples of machine learning techniques but light on detail making implementation of the techniques difficult. Read more
Published 22 months ago by Robbie Clarken
