Mining of Massive Datasets
Author: Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
Publisher: Cambridge University Press
Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Author: Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal
Publisher: Morgan Kaufmann
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book
Machine learning and data mining are rapidly developing fields. Following the success of the first edition of the Encyclopedia of Machine Learning, we are delighted to bring you this updated and expanded edition. We have expanded the scope, as reflected in the revised title Encyclopedia of Machine Learning and Data Mining, to encompass more of the broader activity that surrounds the machine learning process. This includes new articles in such diverse areas as anomaly detection, online controlled experiments, and record linkage as well as substantial expansion of existing entries such as data preparation. We have also included new entries on key recent developments in core machine learning, such as deep learning. A thorough review has also led to updating of much of the existing content. This substantial tome is the product of an intense effort by many individuals. We thank the Editorial Board and the numerous contributors who have provided the content.We are grateful to the Springer team of Andrew Spencer, Michael Hermann, and Melissa Fearon who have shepherded us through the long process of bringing this second edition to print. We are also grateful to the production staff who have turned the content into its final form. We are confident that this revised encyclopedia will consolidate the first edition’s place as a key reference source for the machine learning and data mining communities.
This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.
Python for Finance
Author: Yuxing Yan
Publisher: Packt Publishing Ltd
A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. 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.
Author: James E. Gentle
Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.
From 9/11 to Charlie Hebdo along with Sony-pocalypse and DARPA's $2 million Cyber Grand Challenge, this book examines counterterrorism and cyber security history, strategies and technologies from a thought-provoking approach that encompasses personal experiences, investigative journalism, historical and current events, ideas from thought leaders and the make-believe of Hollywood such as 24, Homeland and The Americans. President Barack Obama also said in his 2015 State of the Union address, "We are making sure our government integrates intelligence to combat cyber threats, just as we have done to combat terrorism. In this new edition, there are seven completely new chapters, including three new contributed chapters by healthcare chief information security officer Ray Balut and Jean C. Stanford, DEF CON speaker Philip Polstra and security engineer and Black Hat speaker Darren Manners, as well as new commentaries by communications expert Andy Marken and DEF CON speaker Emily Peed. The book offers practical advice for businesses, governments and individuals to better secure the world and protect cyberspace.
This volume provides a graduate-level introduction to communication science, including theory and scholarship for masters and PhD students as well as practicing scholars. The work defines communication, reviews its history, and provides a broad look at how communication research is conducted. It also includes chapters reviewing the most frequently addressed topics in communication science. This book presents an overview of theory in general and of communication theory in particular, while offering a broad look at topics in communication that promote understanding of the key issues in communication science for students and scholars new to communication research. The book takes a predominantly "communication science" approach but also situates this approach in the broader field of communication, and addresses how communication science is related to and different from such approaches as critical and cultural studies and rhetoric. As an overview of communication science that will serve as a reference work for scholars as well as a text for the introduction to communication graduate studies course, this volume is an essential resource for understanding and conducting scholarship in the communication discipline.
In-Memory Data Management
Author: Hasso Plattner, Alexander Zeier
Publisher: Springer Science & Business Media
In the last fifty years the world has been completely transformed through the use of IT. We have now reached a new inflection point. This book presents, for the first time, how in-memory data management is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. This book provides the technical foundation for processing combined transactional and analytical operations in the same database. In the year since we published the first edition of this book, the performance gains enabled by the use of in-memory technology in enterprise applications has truly marked an inflection point in the market. The new content in this second edition focuses on the development of these in-memory enterprise applications, showing how they leverage the capabilities of in-memory technology. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes.
Author: D. J. Cooke, H. E. Bez
Publisher: CUP Archive
Mathematics of Computing -- Discrete Mathematics.
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.
Boundary Objects and Beyond
Author: Geoffrey C. Bowker, Stefan Timmermans, Adele E. Clarke, Ellen Balka
Publisher: MIT Press
Susan Leigh Star (1954--2010) was one of the most influential science studies scholars of the last several decades. In her work, Star highlighted the messy practices of discovering science, asking hard questions about the marginalizing as well as the liberating powers of science and technology. In the landmark work Sorting Things Out, Star and Geoffrey Bowker revealed the social and ethical histories that are deeply embedded in classification systems. Star's most celebrated concept was the notion of boundary objects: representational forms -- things or theories -- that can be shared between different communities, with each holding its own understanding of the representation. Unfortunately, Leigh was unable to complete a work on the poetics of infrastructure that further developed the full range of her work. This volume collects articles by Star that set out some of her thinking on boundary objects, marginality, and infrastructure, together with essays by friends and colleagues from a range of disciplines -- from philosophy of science to organization science -- that testify to the wide-ranging influence of Star's work.ContributorsEllen Balka, Eevi E. Beck, Dick Boland, Geoffrey C. Bowker, Janet Ceja Alcalá, Adele E. Clarke, Les Gasser, James R. Griesemer, Gail Hornstein, John Leslie King, Cheris Kramarae, Maria Puig de la Bellacasa, Karen Ruhleder, Kjeld Schmidt, Brian Cantwell Smith, Susan Leigh Star, Anselm L. Strauss, Jane Summerton, Stefan Timmermans, Helen Verran, Nina Wakeford, Jutta Weber