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, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. 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. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book 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. After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.
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About the Author
Ethem Alpaydin is Professor in the Department of Computer Engineering at Bogazici University, Istanbul.
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Customer Reviews
Most Helpful Customer Reviews
4 of 5 people found the following review helpful
Format:Hardcover
This book covers quite a few topics in machine learning, each in a different chapter.
1. Introduction
2. Supervised Learning
3. Bayesian Decision Theory
4. Parametric Methods
5. Multivariate Methods
6. Dimensionality Reduction
7. Clustering
8. Nonparametric Methods
9. Decision Trees
10. Linear Discrimination
11. Multilayer Perceptrons
12. Local Models
13. Hidden Markov Models
14. Assessing and Comparing Classification Algorithms
15. Conbining Multiple Learners
16. Reinforcement Learning
Every chapter has useful notes, references and exercises. The author assumes knowledge of probability and on occasion, skips steps when introducing the formulas. His website has slides and those who have a password can see the solutions to the exercises. Overall, it looks like a good introduction or reference book that covers everything and offers references for further reading.
1. Introduction
2. Supervised Learning
3. Bayesian Decision Theory
4. Parametric Methods
5. Multivariate Methods
6. Dimensionality Reduction
7. Clustering
8. Nonparametric Methods
9. Decision Trees
10. Linear Discrimination
11. Multilayer Perceptrons
12. Local Models
13. Hidden Markov Models
14. Assessing and Comparing Classification Algorithms
15. Conbining Multiple Learners
16. Reinforcement Learning
Every chapter has useful notes, references and exercises. The author assumes knowledge of probability and on occasion, skips steps when introducing the formulas. His website has slides and those who have a password can see the solutions to the exercises. Overall, it looks like a good introduction or reference book that covers everything and offers references for further reading.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
3.9 out of 5 stars
20 reviews
31 of 34 people found the following review helpful
4.0 out of 5 stars
Superb Organization of Ideas!
18 Nov 2006
By Machine Learner
- Published on Amazon.com
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
4.0 out of 5 stars
Good one to start
14 Dec 2005
By Subrat Nanda
- Published on Amazon.com
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
5.0 out of 5 stars
Great book for Learning Machine Learning
16 Oct 2011
By H. Haberdar
- Published on Amazon.com
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.
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3.9 out of 5 stars
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