Written from a computer science perspective by three leading experts in the field, 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. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

    Burke contributes an introductory and summarizing remark, "What is involved, when we say what people are doing and why they are doing it? An answer to that question is the subject of this book. The book is concerned with the basic forms of through which, in accordance with the nature of the world as all men necessarily experience it, are exemplified in the attributing of motives. These forms of though can be embodied profoundly or trivially, truthfully or falsely. They are equally present in systematically elaborated or metaphysical structures, in legal judgments, in poetry and fiction, in political and scientific works, in news and in bits of gossip offered at random."

    In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect.Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor.In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile.The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values.This deeply researched and passionate book could not be more timely.Naomi Klein: "This book is downright scary."Ethan Zuckerman, MIT: "Should be required reading."Dorothy Roberts, author of Killing the Black Body: "A must-read for everyone concerned about modern tools of inequality in America."Astra Taylor, author of The People's Platform: "This is the single most important book about technology you will read this year."

    Rethinking the usage guide for the twenty-first century, Pinker doesn’t carp about the decline of language or recycle pet peeves from the rulebooks of a century ago. Instead, he applies insights from the sciences of language and mind to the challenge of crafting clear, coherent, and stylish prose. In this short, cheerful, and eminently practical book, Pinker shows how writing depends on imagination, empathy, coherence, grammatical knowhow, and an ability to savor and reverse engineer the good prose of others. He replaces dogma about usage with reason and evidence, allowing writers and editors to apply the guidelines judiciously, rather than robotically, being mindful of what they are designed to accomplish. Filled with examples of great and gruesome prose, Pinker shows us how the art of writing can be a form of pleasurable mastery and a fascinating intellectual topic in its own right.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    Web analytics expert Avinash Kaushik, in his thought-provoking style, debunks leading myths and leads you on a path to gaining actionable insights from your analytics efforts. Discover how to move beyond clickstream analysis, why qualitative data should be your focus, and more insights and techniques that will help you develop a customer-centric mindset without sacrificing your company's bottom line. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

    With the printed book, information and ideas could disseminate more widely and effectively than ever before—and in some cases, affect and redirect the sway of history. Today, nearly one million books are published each year. But is the era of the book as we know it—a codex of bound pages—coming to an end? And if it is, should we celebrate its demise and the creation of a democratic digital future, or mourn an irreplaceable loss? The digital age is revolutionizing the information landscape. Already, more books have been scanned and digitized than were housed in the great library in Alexandria, making available millions of texts for a curious reader at the click of a button, and electronic book sales are growing exponentially. Will this revolution in the delivery of information and entertainment make for more transparent and far-reaching dissemination or create a monopolistic stranglehold? In The Case for Books, Robert Darnton, an intellectual pioneer in the field of the history of the book and director of Harvard University's Library, offers an in-depth examination of the book from its earliest beginnings to its shifting role today in popular culture, commerce, and the academy. As an author, editorial advisor, and publishing entrepreneur, Darnton is a unique authority on the life and role of the book in society. This book is a wise work of scholarship—one that requires readers to carefully consider how the digital revolution will broadly affect the marketplace of ideas.

    It covers topics ranging from personal responsibility and career development to architectural techniques for keeping your code flexible and easy to adapt and reuse. Read this book, and you'll learn how toFight software rot; Avoid the trap of duplicating knowledge; Write flexible, dynamic, and adaptable code; Avoid programming by coincidence; Bullet-proof your code with contracts, assertions, and exceptions; Capture real requirements; Test ruthlessly and effectively; Delight your users; Build teams of pragmatic programmers; and Make your developments more precise with automation. Written as a series of self-contained sections and filled with entertaining anecdotes, thoughtful examples, and interesting analogies, The Pragmatic Programmer illustrates the best practices and major pitfalls of many different aspects of software development. Whether you're a new coder, an experienced programmer, or a manager responsible for software projects, use these lessons daily, and you'll quickly see improvements in personal productivity, accuracy, and job satisfaction. You'll learn skills and develop habits and attitudes that form the foundation for long-term success in your career. You'll become a Pragmatic Programmer.

    His fascination with myth is evident in pieces on Ovid's Metamorphoses and the separate odysseys that make up Homer's Odyssey. Three intertwined essays on French utopian socialist Fourier present him as a precursor of Women's Lib, a satirist and visionary thinker whose scheme for a society in which each person's desires could be satisfied deserves to be taken seriously. In other pieces, Calvino brings a fresh, unpredictable approach to why we should reread the classics, how cinema and comic strips influence writers, and the cartoon universe of Saul Steinberg. His message is that writers need to establish erotic communion with the humdrum objects of everyday reality.

    Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business dataUpdates that incorporate input from readers, changes in the field, and more material on statistics and machine learningDozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projectsComplete classroom support for instructors at www.mkp.com/datamining2e companion site

    How can we write or think about disaster when by its very nature it defies speech and compels silence, burns books and shatters meaning? The Writing of the Disaster reflects upon efforts to abide in disaster’s infinite threat. First published in French in 1980, it takes up the most serious tasks of writing: to describe, explain, and redeem when possible, and to admit what is not possible. Neither offers consolation. Maurice Blanchot has been praised on both sides of the Atlantic for his fiction and criticism. The philosopher Emmanuel Levinas once remarked that Blanchot's writing is a "language of pure transcendence, without correlative." Literary theorist and critic Geoffrey Hartman remarked that Blanchot's influence on contemporary writers "cannot be overestimated."

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    The Art of Game Design: A Book of Lenses shows that the same basic principles of psychology that work for board games, card games and athletic games also are the keys to making top-quality video games. Good game design happens when you view your game from many different perspectives, or lenses. While touring through the unusual territory that is game design, this book gives the reader one hundred of these lenses—one hundred sets of insightful questions to ask yourself that will help make your game better. These lenses are gathered from fields as diverse as psychology, architecture, music, visual design, film, software engineering, theme park design, mathematics, writing, puzzle design, and anthropology. Anyone who reads this book will be inspired to become a better game designer—and will understand how to do it.

    Learn You a Haskell for Great Good! introduces programmers familiar with imperative languages (such as C++, Java, or Python) to the unique aspects of functional programming. Packed with jokes, pop culture references, and the author's own hilarious artwork, Learn You a Haskell for Great Good! eases the learning curve of this complex language, and is a perfect starting point for any programmer looking to expand his or her horizons. The well-known web tutorial on which this book is based is widely regarded as the best way for beginners to learn Haskell, and receives over 30,000 unique visitors monthly.

    This book surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use.The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts.The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the "Online Course" link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants.Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience.