An Introduction to Machine Learning

Springer
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This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.
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About the author

Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for more than a quarter century. Over the years, he has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of some 60 program conferences and workshops, and is the member of the editorial boards of three scientific journals. He is widely credited for having co-pioneered research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. Apart from that, he contributed to induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, initialization of neural networks, and other problems.

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Additional Information

Publisher
Springer
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Published on
Jul 15, 2015
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Pages
291
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ISBN
9783319200101
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Language
English
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Genres
Computers / Computer Simulation
Computers / Computer Vision & Pattern Recognition
Computers / Desktop Applications / Design & Graphics
Computers / Intelligence (AI) & Semantics
Computers / Optical Data Processing
Computers / Software Development & Engineering / General
Computers / System Administration / Storage & Retrieval
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Content Protection
This content is DRM protected.
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A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts.

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. 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. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.

Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.

Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projectsOffers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methodsIncludes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Few books in computing have had as profound an influence on software management as Peopleware . The unique insight of this longtime best seller is that the major issues of software development are human, not technical. They’re not easy issues; but solve them, and you’ll maximize your chances of success.

“Peopleware has long been one of my two favorite books on software engineering. Its underlying strength is its base of immense real experience, much of it quantified. Many, many varied projects have been reflected on and distilled; but what we are given is not just lifeless distillate, but vivid examples from which we share the authors’ inductions. Their premise is right: most software project problems are sociological, not technological. The insights on team jelling and work environment have changed my thinking and teaching. The third edition adds strength to strength.”

— Frederick P. Brooks, Jr., Kenan Professor of Computer Science, University of North Carolina at Chapel Hill, Author of The Mythical Man-Month and The Design of Design


“Peopleware is the one book that everyone who runs a software team needs to read and reread once a year. In the quarter century since the first edition appeared, it has become more important, not less, to think about the social and human issues in software develop¿ment. This is the only way we’re going to make more humane, productive workplaces. Buy it, read it, and keep a stock on hand in the office supply closet.”

—Joel Spolsky, Co-founder, Stack Overflow


“When a book about a field as volatile as software design and use extends to a third edition, you can be sure that the authors write of deep principle, of the fundamental causes for what we readers experience, and not of the surface that everyone recognizes. And to bring people, actual human beings, into the mix! How excellent. How rare. The authors have made this third edition, with its additions, entirely terrific.”

—Lee Devin and Rob Austin, Co-authors of The Soul of Design and Artful Making

For this third edition, the authors have added six new chapters and updated the text throughout, bringing it in line with today’s development environments and challenges. For example, the book now discusses pathologies of leadership that hadn’t previously been judged to be pathological; an evolving culture of meetings; hybrid teams made up of people from seemingly incompatible generations; and a growing awareness that some of our most common tools are more like anchors than propellers. Anyone who needs to manage a software project or software organization will find invaluable advice throughout the book.

Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (e.g., adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems. This book is one of the outcomes of the Dagstuhl Seminar 11481 on models@run.time held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.
Accountability. Transparency. Responsibility. These are not words that are often applied to software development.

In this completely revised introduction to Extreme Programming (XP), Kent Beck describes how to improve your software development by integrating these highly desirable concepts into your daily development process.

The first edition of Extreme Programming Explained is a classic. It won awards for its then-radical ideas for improving small-team development, such as having developers write automated tests for their own code and having the whole team plan weekly. Much has changed in five years. This completely rewritten second edition expands the scope of XP to teams of any size by suggesting a program of continuous improvement based on:

Five core values consistent with excellence in software development Eleven principles for putting those values into action Thirteen primary and eleven corollary practices to help you push development past its current business and technical limitations

Whether you have a small team that is already closely aligned with your customers or a large team in a gigantic or multinational organization, you will find in these pages a wealth of ideas to challenge, inspire, and encourage you and your team members to substantially improve your software development.

You will discover how to:

Involve the whole team–XP style Increase technical collaboration through pair programming and continuous integration Reduce defects through developer testing Align business and technical decisions through weekly and quarterly planning Improve teamwork by setting up an informative, shared workspace

You will also find many other concrete ideas for improvement, all based on a philosophy that emphasizes simultaneously increasing the humanity and effectiveness of software development.

Every team can improve. Every team can begin improving today. Improvement is possible–beyond what we can currently imagine. Extreme Programming Explained, Second Edition, offers ideas to fuel your improvement for years to come.



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