The timely provision of relevant information with minimal 'noise' is critical to modern society and this is what information retrieval (IR) is all about. It is a dynamic subject, with current changes driven by the expansion of the World Wide Web, the advent of modern and inexpensive graphical user interfaces and the development of reliable and low-cost mass storage devices. Modern Information Retrieval discusses all these changes in great detail and can be used for a first course on IR as well as graduate courses on the topic.The organization of the book, which includes a comprehensive glossary, allows the reader to either obtain a broad overview or detailed knowledge of all the key topics in modern IR. The heart of the book is the nine chapters written by Baeza-Yates and Ribeiro-Neto, two leading exponents in the field. For those wishing to delve deeper into key areas there are further state-of-the-art ch

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, 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 models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This 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, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    

    French, Spanish, Italian, and Romanian are among its direct descendants, and countless Latin words and phrases comprise the cornerstone of English itself. A Natural History or Latin tells its history from its origins over 2500 years ago to the present. Brilliantly conceived, popularizing but authoritative, and written with the fluency and light touch that have made Tore Janson's Speak so attractive to tens of thousands of readers, it is a masterpiece of adroit synthesis.The book commences with a description of the origins, emergence, and dominance of Latin over the Classical period. Then follows an account of its survival through the Middle Ages into modern times, with emphasis on its evolution throughout the history, culture, and religious practices of Medieval Europe. By judicious quotation of Latin words, phrases, and texts the author illustrates how the written and spoken language changed, region by region over time; how it met resistance from native languages; and how therefore some entire languages disappeared. Janson offers a vivid demonstration of the value of Latin as a means of access to a vibrant past and a persuasive argument for its continued worth. A concise and easy-to-understand introduction to Latin grammar and a list of the most frequent Latin words, including 500 idioms and phrases still in common use, complement the work.

     Offers extensively updated coverage, new problem sets, and chapter-ending material to enhance the book’s relevance to today’s engineers and scientists. Includes new problem sets demonstrating updated applications to engineering as well as biological, physical, and computer science. Emphasizes key ideas as well as the risks and hazards associated with practical application of the material. Includes new material on topics including: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; “dummy” variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and lognormal distributions; central limit theorem, and data plotting. Introduces Bayesian statistics, including its applications to many fields. For those interested in learning more about probability and statistics.

    The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

    It provides a complete overview of hacking, cracking, and their effect on the world. You'll learn about the prerequisites for hacking, the various types of hackers, and the many kinds of hacking attacks: Active Attacks Masquerade Attacks Replay Attacks Modification of Messages Denial of Service or DoS Spoofing Techniques Mobile Hacking Hacking Tools Penetration Testing Passive Attacks If you are looking to venture into the world of hacking, this book will teach you all the information you need to know. When you download Hacking: Ultimate Hacking For Beginners - How to Hack, you'll discover how to acquire Many Powerful Hacking Tools. You'll also learn about Malware: A Hacker’s Henchman and Common Attacks And Viruses. You'll even learn about identity theft, how to protect yourself, and how hackers profit from this information! Read this book for FREE on Kindle Unlimited - Download NOW! Download Hacking: Ultimate Hacking For Beginners - How to Hack right away - This Amazing 4th Edition puts a wealth of knowledge at your disposal. You'll learn how to hack an email password, spoofing techniques, mobile hacking, and tips for ethical hacking. You'll even learn how to fight viruses and choose the right antivirus software for your system! Just scroll to the top of the page and select the Buy Button. Download Your Copy TODAY!

    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

    This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts. You get a firm grasp of the fundamentals and a hands-on understanding of how to apply them before moving on to the more advanced material that follows. Each chapter presents you with easy-to-follow descriptions illustrated by graphics, formulas, and plenty of solved examples. Before you know it, you'll learn to apply statistical reasoning and statistical techniques, from basic concepts of probability and hypothesis testing to multivariate analysis. Organized into four distinct sections, Statistics in a Nutshell offers you:Introductory material: Different ways to think about statistics Basic concepts of measurement and probability theoryData management for statistical analysis Research design and experimental design How to critique statistics presented by others Basic inferential statistics: Basic concepts of inferential statistics The concept of correlation, when it is and is not an appropriate measure of association Dichotomous and categorical data The distinction between parametric and nonparametric statistics Advanced inferential techniques: The General Linear Model Analysis of Variance (ANOVA) and MANOVA Multiple linear regression Specialized techniques: Business and quality improvement statistics Medical and public health statistics Educational and psychological statistics Unlike many introductory books on the subject, Statistics in a Nutshell doesn't omit important material in an effort to dumb it down. And this book is far more practical than most college texts, which tend to over-emphasize calculation without teaching you when and how to apply different statistical tests. With Statistics in a Nutshell, you learn how to perform most common statistical analyses, and understand statistical techniques presented in research articles. If you need to know how to use a wide range of statistical techniques without getting in over your head, this is the book you want.

    E. Moore, and Ludwig Wittgenstein in the four decades around the turn of the twentieth century, analytic philosophy established itself in various forms in the 1930s. After the Second World War, it developed further in North America, in the rest of Europe, and is now growing in influence as the dominant philosophical tradition right across the world, from Latin America to East Asia.In this Very Short Introduction Michael Beaney introduces some of the key ideas of the founders of analytic philosophy by exploring certain fundamental philosophical questions and showing how those ideas can be used in offering answers. Considering the work of Susan Stebbing, he also explores the application of analytic philosophy to critical thinking, and emphasizes the conceptual creativity that lies at the heart of fruitful analysis. Throughout, Beaney illustrates why clarity of thinking, precision of expression, and rigour of argumentation are rightly seen as virtues of analytic philosophy.

    Think of the familiar map of the New York City subway system, or a diagram of the human brain. Successful visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding.This book examines the methods of two dozen visualization experts who approach their projects from a variety of perspectives -- as artists, designers, commentators, scientists, analysts, statisticians, and more. Together they demonstrate how visualization can help us make sense of the world.Explore the importance of storytelling with a simple visualization exerciseLearn how color conveys information that our brains recognize before we're fully aware of itDiscover how the books we buy and the people we associate with reveal clues to our deeper selvesRecognize a method to the madness of air travel with a visualization of civilian air trafficFind out how researchers investigate unknown phenomena, from initial sketches to published papers Contributors include:Nick Bilton, Michael E. Driscoll, Jonathan Feinberg, Danyel Fisher, Jessica Hagy, Gregor Hochmuth, Todd Holloway, Noah Iliinsky, Eddie Jabbour, Valdean Klump, Aaron Koblin, Robert Kosara, Valdis Krebs, JoAnn Kuchera-Morin et al., Andrew Odewahn, Adam Perer, Anders Persson, Maximilian Schich, Matthias Shapiro, Julie Steele, Moritz Stefaner, Jer Thorp, Fernanda Viegas, Martin Wattenberg, and Michael Young.

    This provocative book undertakes a new and challenging reading of recent semiotic and structuralist theory, arguing that films, novels, and poems cannot be studied in isolation from their viewers and readers.

    One of the most distinguishing features of the show is the innovative way its writers play with language--fabricating new words, morphing existing ones, and throwing usage on its head. The result has been a strikingly resonant lexicon that reflects the power of both youth culture and television in the evolution of American slang. Using the show to illustrate how new slang is formed, transformed, and transmitted, Slayer Slang is one of those rare books that combines a serious explanation of a pop culture phenomenon with an engrossing read for Buffy fans, language mavens, and pop culture critics. Noted linguist Michael Adams offers a synopsis of the program's history, an essay on the nature and evolution of the show's language, and a detailed glossary of slayer slang, annotated with actual dialogue. Introduced by Jane Espenson, one of the show's most inventive writers (and herself a linguist), Slayer Slang offers a quintessential example of contemporary youth culture serving as a vehicle for slang.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.