How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

    With all our media now traveling a single network, an unprecedented potential is building for centralized control over what Americans see and hear. Could history repeat itself with the next industrial consolidation? Could the Internet—the entire flow of American information—come to be ruled by one corporate leviathan in possession of “the master switch”? That is the big question of Tim Wu’s pathbreaking book.As Wu’s sweeping history shows, each of the new media of the twentieth century—radio, telephone, television, and film—was born free and open. Each invited unrestricted use and enterprising experiment until some would-be mogul battled his way to total domination. Here are stories of an uncommon will to power, the power over information: Adolph Zukor, who took a technology once used as commonly as YouTube is today and made it the exclusive prerogative of a kingdom called Hollywood . . . NBC’s founder, David Sarnoff, who, to save his broadcast empire from disruptive visionaries, bullied one inventor (of electronic television) into alcoholic despair and another (this one of FM radio, and his boyhood friend) into suicide . . . And foremost, Theodore Vail, founder of the Bell System, the greatest information empire of all time, and a capitalist whose faith in Soviet-style central planning set the course of every information industry thereafter.Explaining how invention begets industry and industry begets empire—a progress often blessed by government, typically with stifling consequences for free expression and technical innovation alike—Wu identifies a time-honored pattern in the maneuvers of today’s great information powers: Apple, Google, and an eerily resurgent AT&T. A battle royal looms for the Internet’s future, and with almost every aspect of our lives now dependent on that network, this is one war we dare not tune out.Part industrial exposé, part meditation on what freedom requires in the information age, The Master Switch is a stirring illumination of a drama that has played out over decades in the shadows of our national life and now culminates with terrifying implications for our future.

    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.

    The manner in which computers are now able to mimic human thinking is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the input of data they are able to form their own decisions and take actions virtually as a human would. But as a machine, can consider many more scenarios and execute calculations to solve complex problems. This is the element that excites companies and budding machine learning engineers the most. The ability to solve complex problems never before attempted. This is also perhaps one reason why you are looking at purchasing this book, to gain a beginner's introduction to machine learning. This book provides a plain English introduction to the following topics: - Artificial Intelligence - Big Data - Downloading Free Datasets - Regression - Support Vector Machine Algorithms - Deep Learning/Neural Networks - Data Reduction - Clustering - Association Analysis - Decision Trees - Recommenders - Machine Learning Careers This book has recently been updated following feedback from readers. Version II now includes: - New Chapter: Decision Trees - Cleanup of minor errors

    

    The world has changed since then. Today it is clear that "good old-fashioned AI," based on the idea of using symbolic representations to produce general intelligence, is in decline (although several believers still pursue its pot of gold), and the focus of the AI community has shifted to more complex models of the mind. It has also become more common for AI researchers to seek out and study philosophy. For this edition of his now classic book, Dreyfus has added a lengthy new introduction outlining these changes and assessing the paradigms of connectionism and neural networks that have transformed the field. At a time when researchers were proposing grand plans for general problem solvers and automatic translation machines, Dreyfus predicted that they would fail because their conception of mental functioning was naive, and he suggested that they would do well to acquaint themselves with modern philosophical approaches to human being. "What Computers Still Can't Do" was widely attacked but quietly studied. Dreyfus's arguments are still provocative and focus our attention once again on what it is that makes human beings unique.

    As an AI researcher with 25 years of experience, professor Mike Wooldridge has learned to be obsessively cautious about such claims, while still promoting an intense optimism about the future of the field. There have been genuine scientific breakthroughs that have made AI systems possible in the past decade that the founders of the field would have hailed as miraculous. Driverless cars and automated translation tools are just two examples of AI technologies that have become a practical, everyday reality in the past few years, and which will have a huge impact on our world.While the dream of conscious machines remains, Professor Wooldridge believes, a distant prospect, the floodgates for AI have opened. Wooldridge's A Brief History of Artificial Intelligence is an exciting romp through the history of this groundbreaking field--a one-stop-shop for AI's past, present, and world-changing future.

    Along the tidal rivers of Malaysia, thousands of fireflies congregate and flash in unison; the moon spins in perfect resonance with its orbit around the earth; our hearts depend on the synchronous firing of ten thousand pacemaker cells. While the forces that synchronize the flashing of fireflies may seem to have nothing to do with our heart cells, there is in fact a deep connection. Synchrony is a science in its infancy, and Strogatz is a pioneer in this new frontier in which mathematicians and physicists attempt to pinpoint just how spontaneous order emerges from chaos. From underground caves in Texas where a French scientist spent six months alone tracking his sleep-wake cycle, to the home of a Dutch physicist who in 1665 discovered two of his pendulum clocks swinging in perfect time, this fascinating book spans disciplines, continents, and centuries. Engagingly written for readers of books such as Chaos and The Elegant Universe, Sync is a tour-de-force of nonfiction writing.

    Gordon strips engineering of its confusing technical terms, communicating its founding principles in accessible, witty prose.For anyone who has ever wondered why suspension bridges don't collapse under eight lanes of traffic, how dams hold back--or give way under--thousands of gallons of water, or what principles guide the design of a skyscraper, a bias-cut dress, or a kangaroo, this book will ease your anxiety and answer your questions.Structures: Or Why Things Don't Fall Down is an informal explanation of the basic forces that hold together the ordinary and essential things of this world--from buildings and bodies to flying aircraft and eggshells. In a style that combines wit, a masterful command of his subject, and an encyclopedic range of reference, Gordon includes such chapters as "How to Design a Worm" and "The Advantage of Being a Beam," offering humorous insights in human and natural creation.Architects and engineers will appreciate the clear and cogent explanations of the concepts of stress, shear, torsion, fracture, and compression. If you're building a house, a sailboat, or a catapult, here is a handy tool for understanding the mechanics of joinery, floors, ceilings, hulls, masts--or flying buttresses.Without jargon or oversimplification, Structures opens up the marvels of technology to anyone interested in the foundations of our everyday lives.

    But ask what it means, and the result will be a brawl. For a century, most physicists have followed Niels Bohr's Copenhagen interpretation and dismissed questions about the reality underlying quantum physics as meaningless. A mishmash of solipsism and poor reasoning, Copenhagen endured, as Bohr's students vigorously protected his legacy, and the physics community favored practical experiments over philosophical arguments. As a result, questioning the status quo long meant professional ruin. And yet, from the 1920s to today, physicists like John Bell, David Bohm, and Hugh Everett persisted in seeking the true meaning of quantum mechanics. What Is Real? is the gripping story of this battle of ideas and of the courageous scientists who dared to stand up for truth.

    But building a machine learning system requires that you make practical decisions: Should you collect more training data? Should you use end-to-end deep learning? How do you deal with your training set not matching your test set? and many more. Historically, the only way to learn how to make these "strategy" decisions has been a multi-year apprenticeship in a graduate program or company. This is a book to help you quickly gain this skill, so that you can become better at building AI systems.

    Soon, few of us will own our own automobiles and instead will get around in driverless electric vehicles that we summon with the touch of an app. We will be liberated from driving, prevent over 90% of car crashes, provide freedom of mobility to the elderly and disabled, and decrease our dependence on fossil fuels. Autonomy is the story of the maverick engineers and computer nerds who are creating the revolution. Longtime advisor to the Google Self-Driving Car team and former GM research and development chief Lawrence D. Burns provides the perfectly-timed history of how we arrived at this point, in a character-driven and heavily reported account of the unlikely thinkers who accomplished what billion-dollar automakers never dared.Beginning with the way 9/11 spurred the U.S. government to set a million-dollar prize for a series of off-road robot races in the Mojave Desert up to the early 2016 stampede to develop driverless technology, Autonomy is a page-turner that represents a chronicle of the past, diagnosis of the present, and prediction of the future—the ultimate guide to understanding the driverless car and navigating the revolution it sparks.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    In interviews with more than a dozen leading scientists and thinkers - including former astronaut Mike Massimino, iRobot co-founder Rodney Brooks, Dava Newman (former deputy administrator of NASA), Oren Etzioni (CEO, Allen Institute for Artificial Intelligence), futurist Kate Compton, and Caleb Harper (director of the Open Agriculture initiative at MIT) - Savage explores the mind-blowing and often misunderstood ways in which science and innovation are transforming the way we live, work, and play.Full of wonder, optimism, and plain old awesomeness, What Happens Next will be a revelation for anyone who’s ever wondered about what our future will look like and how we’ll get there. 2 hours 50 minutes

    This book teaches you how to conduct examinations by explaining what digital forensics is, the methodologies used, key technical concepts and the tools needed to perform examinations. Details on digital forensics for computers, networks, cell phones, GPS, the cloud, and Internet are discussed. Readers will also learn how to collect evidence, document the scene, and recover deleted data. This is the only resource your students need to get a jump-start into digital forensics investigations.This book is organized into 11 chapters. After an introduction to the basics of digital forensics, the book proceeds with a discussion of key technical concepts. Succeeding chapters cover labs and tools; collecting evidence; Windows system artifacts; anti-forensics; Internet and email; network forensics; and mobile device forensics. The book concludes by outlining challenges and concerns associated with digital forensics. PowerPoint lecture slides are also available.This book will be a valuable resource for entry-level digital forensics professionals as well as those in complimentary fields including law enforcement, legal, and general information security.