Machine Learning Books

Programming Collective Intelligence: Building Smart Web 2.0 Applications

Toby Segaran

Amazon Price: $26.39 List Price: $39.99 Usually ships in 24 hours By: O'Reilly Media, Inc. Amazon Marketplace: 44 new & used starting at $22.81

Buy at Amazon.com

Browse similar items by category:
Subjects -> Computers & Internet -> Programming -> Algorithms -> General
Subjects -> Computers & Internet -> Programming -> General
Subjects -> Computers & Internet -> Programming -> Languages & Tools -> General

Customer Reviews:
Total reviews: 34 Average rating: 4.5 of 5

Editorial Review:

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in adataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

Pattern Recognition and Machine Learning (Information Science and Statistics)

Christopher M. Bishop

Amazon Price: $58.86 List Price: $84.95 Usually ships in 24 hours By: Springer Amazon Marketplace: 49 new & used starting at $49.00

Buy at Amazon.com

Browse similar items by category:
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> General
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> Machine Learning
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> Machine Vision

Customer Reviews:
Total reviews: 41 Average rating: 4.0 of 5

Editorial Review:

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.

This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

Coming soon:

*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)

*For instructors, worked solutions to remaining exercises from the Springer web site

*Lecture slides to accompany each chapter

*Data sets available for download

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Ian H. Witten, Eibe Frank

Amazon Price: $41.55 List Price: $65.95 Usually ships in 24 hours By: Morgan Kaufmann Amazon Marketplace: 48 new & used starting at $36.82

Buy at Amazon.com

Browse similar items by category:
Subjects -> Computers & Internet -> Databases -> Data Mining
Subjects -> Computers & Internet -> Databases -> General
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> General

Customer Reviews:
Total reviews: 25 Average rating: 4.0 of 5

Editorial Review:

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.

The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.

* Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface

The Elements of Statistical Learning

T. Hastie, R. Tibshirani, J. H. Friedman

Amazon Price: $75.17 List Price: $94.00 Usually ships in 24 hours By: Springer Amazon Marketplace: 50 new & used starting at $61.00

Buy at Amazon.com

Browse similar items by category:
Subjects -> Computers & Internet -> Databases -> Data Mining
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> General
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> Machine Learning

Customer Reviews:
Total reviews: 27 Average rating: 4.0 of 5

Editorial Review:

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Machine Learning (Mcgraw-Hill International Edit)

Thomas Mitchell

Amazon Price: $79.45 List Price: $79.45 Usually ships in 2 to 3 weeks By: McGraw Hill Higher Education Amazon Marketplace: 20 new & used starting at $49.49

Buy at Amazon.com

Browse similar items by category:
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> General
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> Machine Learning
Subjects -> Computers & Internet -> General

Customer Reviews:
Total reviews: 34 Average rating: 4.5 of 5

Great Start to Machine Learning 5 out of 5 stars.
0 of 0 people found this review helpful.

I have used this book during my masters and found it to be an extremely helpful and a gentle introduction to the thick and things of machine learning applications. The various chapters are nicely paced with helpful problems at the end. Another great thing about the book is treatment of detailed examples with each concept and that the author carefully ties various concepts as they arise, with not just new, but also examples from previous chapters, which helps the user to understand different concepts applied to same problems thereby making clear difference between different methods. Also the author has a dedicated website with updated errata and notes, which is also very helpful! Having said that, I think the book is an introduction to various machine learning methods and one can easily follow on the references listed for detailed treatment of relevant topics.

Editorial Review:

This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning--including probability and statistics, artificial intelligence, and neural networks--unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students.

Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)

Bing Liu

Amazon Price: $47.96 List Price: $59.95 Usually ships in 24 hours By: Springer Amazon Marketplace: 33 new & used starting at $47.96

Buy at Amazon.com

Browse similar items by category:
Subjects -> Computers & Internet -> Databases -> Data Mining
Subjects -> Computers & Internet -> Databases -> General
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> General

Customer Reviews:
Total reviews: 1 Average rating: 5.0 of 5

Editorial Review:

Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques.

Liu has written a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered both in breadth and in depth. His book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text.

The book offers a rich blend of theory and practice, addressing seminal research ideas, as well as examining the technology from a practical point of view. It is suitable for students, researchers and practitioners interested in Web mining both as a learning text and a reference book. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)

Richard S. Sutton, Andrew G. Barto

Amazon Price: $48.00 List Price: $60.00 Usually ships in 24 hours By: The MIT Press Amazon Marketplace: 28 new & used starting at $37.00

Buy at Amazon.com

Browse similar items by category:
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> General
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> Machine Learning
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> Theory of Computing

Customer Reviews:
Total reviews: 12 Average rating: 4.5 of 5

Editorial Review:

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.

The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

Bernhard Schlkopf, Alexander J. Smola

Amazon Price: $54.00 List Price: $75.00 Usually ships in 24 hours By: The MIT Press Amazon Marketplace: 21 new & used starting at $50.00

Buy at Amazon.com

Browse similar items by category:
Subjects -> Computers & Internet -> Programming -> Algorithms -> General
Subjects -> Computers & Internet -> Programming -> General
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> General

Customer Reviews:
Total reviews: 8 Average rating: 5.0 of 5

Editorial Review:

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.

Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Introduction to the Theory of Computation

Michael Sipser

List Price: $140.95 By: Course Technology Amazon Marketplace: 36 new & used starting at $9.00

Buy at Amazon.com

Browse similar items by category:
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> Machine Learning
Subjects -> Computers & Internet -> General
Subjects -> Science -> General

Customer Reviews:
Total reviews: 51 Average rating: 4.5 of 5

dont buy this version 2 out of 5 stars.
4 of 8 people found this review helpful.

Go buy an international version which is a lot cheaper than this, and they have the same contents. This version is also printed in Black and White and the paper is really cheap. Don't make a mistake like me buying same product for 90$ more. Again, the only difference between hardcover and softcover(international ver) is the price.

Editorial Review:

"Intended as an upper-level undergraduate or introductory graduate text in computer science theory," this book lucidly covers the key concepts and theorems of the theory of computation. The presentation is remarkably clear; for example, the "proof idea," which offers the reader an intuitive feel for how the proof was constructed, accompanies many of the theorems and a proof. Introduction to the Theory of Computation covers the usual topics for this type of text plus it features a solid section on complexity theory--including an entire chapter on space complexity. The final chapter introduces more advanced topics, such as the discussion of complexity classes associated with probabilistic algorithms.

Bioconductor Case Studies (Use R)

Florian Hahne, Wolfgang Huber, Robert Gentleman, Seth Falcon

Amazon Price: $53.14 List Price: $64.95 Usually ships in 24 hours By: Springer Amazon Marketplace: 12 new & used starting at $53.14

Buy at Amazon.com

Browse similar items by category:
Subjects -> Computers & Internet -> Computer Science -> Artificial Intelligence -> Machine Learning
Subjects -> Science -> Biological Sciences -> Biology -> General
Subjects -> Science -> Biological Sciences -> Bioinformatics

Editorial Review:

Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include

* import and preprocessing of data from various sources

* statistical modeling of differential gene expression

* biological metadata

* application of graphs and graph rendering

* machine learning for clustering and classification problems

* gene set enrichment analysis

Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table.