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"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
Book Description
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.
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Most helpful customer reviews
1 of 1 people found the following review helpful
4.0 out of 5 stars
Good Introduction, Albeit Theoretical,
By
This review is from: Introduction to Machine Learning (Hardcover)
Introduction to Machine Learning gives a good, very theoretical, on several machine learning topics, such as Bayesian classification, clustering, decision trees, multilayer perceptrons(MLPs), kernel machines, hidden Markov models and boosting.However, as the book covers a lot of ground, don't expect to be handheld, to read about implementation or practical details; this is only a theoretical overview. For example, MLPs are often used for character recognition but the book does not give any examples as to how you would use one to recognize characters; the reader is assumed to be able to figure out what would be good inputs for the MLP.
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Most Helpful Customer Reviews on Amazon.com (beta) Amazon.com:
4.0 out of 5 stars (7 customer reviews) 6 of 6 people found the following review helpful
5.0 out of 5 stars
Great book for Learning Machine Learning,
By H. Haberdar - Published on Amazon.com
Amazon Verified Purchase(What's this?)
This review is from: Introduction to Machine Learning (Hardcover)
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
5.0 out of 5 stars
Good for beginner,
By Emre Demir - Published on Amazon.com
This review is from: Introduction to Machine Learning (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
4.0 out of 5 stars
A good introduction with a few minor issues,
By Some_guy - Published on Amazon.com
This review is from: Introduction to Machine Learning (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. |
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