These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    In addition to changing how we live, these tools enable us to collect and process data about human behavior on a scale never before imaginable, offering entirely new approaches to core questions about social behavior. Bit by Bit is the key to unlocking these powerful methods--a landmark book that will fundamentally change how the next generation of social scientists and data scientists explores the world around us.Bit by Bit is the essential guide to mastering the key principles of doing social research in this fast-evolving digital age. In this comprehensive yet accessible book, Matthew Salganik explains how the digital revolution is transforming how social scientists observe behavior, ask questions, run experiments, and engage in mass collaborations. He provides a wealth of real-world examples throughout and also lays out a principles-based approach to handling ethical challenges.Bit by Bit is an invaluable resource for social scientists who want to harness the research potential of big data and a must-read for data scientists interested in applying the lessons of social science to tomorrow's technologies.Illustrates important ideas with examples of outstanding researchCombines ideas from social science and data science in an accessible style and without jargonGoes beyond the analysis of "found" data to discuss the collection of "designed" data such as surveys, experiments, and mass collaborationFeatures an entire chapter on ethicsIncludes extensive suggestions for further reading and activities for the classroom or self-study

    Professor Douglas makes points which illuminate matters in the philosophy of religion and the philosophy of science and help to show the rest of us just why and how anthropology has become a fundamentally intellectual discipline.

    When she goes to visit him and takes a tour of Oxford and Rhodes House, she begins questioning her belief in a system she has previously revered, as she learns the truth behind what Virginia Woolf described almost a century earlier as the 'stream of gold and silver' that flows through elite institutions and dictates decisions about who deserves to be educated there. The question that forms in her mind drives the following two years of conversations and investigations: who gets to be smart?Interrogating the adage, 'knowledge is power', and calling institutional prejudice to account, Bri once again dives into her own privilege and presumptions to bring us the stark and confronting results. Far from offering any 'equality of opportunity', Australia's education system exacerbates social stratification. The questions Bri asks of politics and society have their answers laid bare in the response to the Ramsay Centre for Western Civilisation, COVID-19, and the Black Lives Matter protests of 2020.

    It is a collection of essays by one of Canada's greatest historians, on a subject that opened broad new avenues of thought on the role of media in the creation of history. Marshall McLuhan, deeply influenced by these essays, led North America to a new awareness of the role of media in contemporary culture. The works of Harold Innis are seminal in the study of Canadian history; the essays in this volume continue to generate intense dabate among historians, communications scholars, and media theorists.

    

    Today’s "machine-learning" systems, trained by data, are so effective that we’ve invited them to see and hear for us?and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem.Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole?and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands.The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software.In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story.The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.

    Jameson’s inquiry looks at the postmodern across a wide landscape, from “high” art to “low” from market ideology to architecture, from painting to “punk” film, from video art to literature.

    Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

    Presenting the concept of the "New Jim Code," she shows how a range of discriminatory designs encode inequity by explicitly amplifying racial hierarchies; by ignoring but thereby replicating social divisions; or by aiming to fix racial bias but ultimately doing quite the opposite. Moreover, she makes a compelling case for race itself as a kind of technology, designed to stratify and sanctify social injustice in the architecture of everyday life.This illuminating guide provides conceptual tools for decoding tech promises with sociologically informed skepticism. In doing so, it challenges us to question not only the technologies we are sold but also the ones we ourselves manufacture.If you adopt this book for classroom use in the 2019-2020 academic year, the author would be pleased to arrange to Skype to a session of your class. If interested, enter your details in this sign-up sheet https: //buff.ly/2wJsvZr

    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.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    In the bustling, cluttered arcades, street and interior merge and historical time is broken up into kaleidoscopic distractions and displays of ephemera. Here, at a distance from what is normally meant by "progress," Benjamin finds the lost time(s) embedded in the spaces of things.

    THIS BOOK has been written to cover BOTH the current ("2nd generation") Kindle Fire HD, AND the earlier ("1st Generation") Kindle Fire HD.<br><br></h2><br><br><h2>"Fantastic..." "Great Help..." "Easy for a non-geek to understand." -Actual reviewer comments for Kindle Fire HD Tips, Tricks, and Traps: A How-To Tutorial for the Kindle Fire HD</h2><br><i><br>"Fantastic! I searched and searched for a source to help me better understand my new Kindle. I was about to give up and then I found this book. I have discovered so many tips and tricks! I am enjoying my Kindle so much more!"<br><br>"Easy for a non-geek to understand. Thanks for writing a book that I can understand. Very basic guide to the kindle fire that is easy to follow and makes it easy to implement any suggestions offered. The directions given matched what is actually on my kindle fire. I have read a couple of books that were supposedly updated for late 2012 or for January 2013 that gave instructions for actions on my kindle fire that didn't match what I see on my device bough in December 2012. This is hugely frustrating to a tech novice. This book told me exactly where to go and what to do."<br><br>"Great help. This was a big help with my first venture in tablet land. A lot of good ideas. A must read for any kindle user."<br></i><br><br>So, you've got a Kindle Fire as a gift, or perhaps you bit the purchase bullet on your own because you wanted this impressive tablet. Do you want to get the most out of your new Kindle Fire HD? If you are looking for a top-notch tutorial at a reasonable cost, you've come to the right place! Here is the book that will teach you 100% of what you need to know. <b>Kindle Fire HD Tips, Tricks, and Traps: A How-To Tutorial for the Kindle Fire HD</b> is your detailed guide to getting the maximum benefit from your Kindle Fire HD.<br>In this comprehensive guide, you'll learn tips (ways to effectively use your Kindle Fire), tricks (ways to improve the operation of your Kindle Fire), and traps (things to avoid to prevent problems while using your Kindle Fire). You will learn-<br><br>• How to get around within the user interface, the home screen, and the carousel more efficiently<br><br>• How to make your Kindle Fire your own, customizing its display and operation for fastest and easiest use<br><br>• How to find THOUSANDS of FREE books, as well as movies and songs, for your Kindle Fire<br><br>• How to setup the security options to protect your account information<br><br>• How you can move your iTunes or other music library to your Kindle Fire<br><br>• How you can download YouTube videos to your Kindle Fire<br><br>* How to use the built-in camera and the new camera app provided by Amazon in a late 2012 software update<br><br>• Suggested apps that no Kindle Fire owner should be without<br><br>You will learn all of the above and more, with Kindle Fire Tips, Tricks, and Traps: A How-To Tutorial for the Kindle Fire HD as a part of your library.

    Bennett argues that political theory needs to do a better job of recognizing the active participation of nonhuman forces in events. Toward that end, she theorizes a “vital materiality” that runs through and across bodies, both human and nonhuman. Bennett explores how political analyses of public events might change were we to acknowledge that agency always emerges as the effect of ad hoc configurations of human and nonhuman forces. She suggests that recognizing that agency is distributed this way, and is not solely the province of humans, might spur the cultivation of a more responsible, ecologically sound politics: a politics less devoted to blaming and condemning individuals than to discerning the web of forces affecting situations and events.Bennett examines the political and theoretical implications of vital materialism through extended discussions of commonplace things and physical phenomena including stem cells, fish oils, electricity, metal, and trash. She reflects on the vital power of material formations such as landfills, which generate lively streams of chemicals, and omega-3 fatty acids, which can transform brain chemistry and mood. Along the way, she engages with the concepts and claims of Spinoza, Nietzsche, Thoreau, Darwin, Adorno, and Deleuze, disclosing a long history of thinking about vibrant matter in Western philosophy, including attempts by Kant, Bergson, and the embryologist Hans Driesch to name the “vital force” inherent in material forms. Bennett concludes by sketching the contours of a “green materialist” ecophilosophy.