Designed for graduate and advanced undergraduate students, it includes the contributions of leading thinkers, educators, and practitioners who provide an in-depth and objective appraisal of why and how we organize health care the way we do; the enormous impact of health-related behaviors on the structure, function, and cost of the health care delivery system; and other emerging and recurrent issues in health policy, health care management, and public health. To update this book with the rapid changes that have occurred in health care through November 2013, a separate chapter, the Affordable Care Act (ACA) Supplement, is available to students and instructors as a downloadable PDF.This text is divided into five sections, in order to provide some coherence to this broad terrain. Part I, The Current U.S. Health Care System, addresses major characteristics and issues, including reform, financing, and comparative health care systems. This section now includes multiple new charts and tables providing concrete health care data. Part II, Population Health, focuses on health behavior, including health care models, public health policy and practice, risk factors, facilitating healthy lifestyle practices, and access to care. Part III, Medical Care Delivery, addresses integrated health models, delivering high-quality health care, health care costs and value, and comparative effectiveness. Part IV, Support for Medical Care Delivery, concerns governance and management issues, including accountability, the health workforce, and information technology. Part V, The Future of Health Care Delivery in the United States, includes a new 5-year trend forecast.Key Features: Includes major provisions of the Patient Protection and Affordable Health Care Act of 2010Each chapter includes these special features: key concepts; extensive mapping resources; key words; learning objectives; discussion questions; and case studiesCovers the newest models of care, such as Accountable Care Organizations and Integrated Delivery SystemsExamines new ways of conceptualizing and assessing health care, including comparative effectiveness researchFeatures contributions by leading scholars and key figures within the U.S. health care system, including John Billings, JD; Carolyn M. Clancy, MD; C. Tracy Orleans, PhD; and Michael S. Sparer, PhD, JDContains new coverage of health reform, developing countries, population health, public health and catastrophic events, and a broadened discussion of the health care workforceAffordable Care Act (ACA) Supplement available to students and instructors as a downloadable PDF. Available to Instructors: Instructor's Guide (updated to reflect content from ACA supplement)PowerPoint PresentationsImage BankTest Bank (updated to reflect content from ACA supplement)"

    In " An Introduction to the Event-Related Potential Technique," Steve Luck offers the first comprehensive guide to the practicalities of conducting ERP experiments in cognitive neuroscience and related fields, including affective neuroscience and experimental psychopathology. The book can serve as a guide for the classroom or the laboratory and as a reference for researchers who do not conduct ERP studies themselves but need to understand and evaluate ERP experiments in the literature. It summarizes the accumulated body of ERP theory and practice, providing detailed, practical advice about how to design, conduct, and interpret ERP experiments, and presents the theoretical background needed to understand why an experiment is carried out in a particular way. Luck focuses on the most fundamental techniques, describing them as they are used in many of the world's leading ERP laboratories. These techniques reflect a long history of electrophysiological recordings and provide an excellent foundation for more advanced approaches.The book also provides advice on the key topic of how to design ERP experiments so that they will be useful in answering questions of broad scientific interest. This reflects the increasing proportion of ERP research that focuses on these broader questions rather than the "ERPology" of early studies, which concentrated primarily on ERP components and methods. Topics covered include the neural origins of ERPs, signal averaging, artifact rejection and correction, filtering, measurement and analysis, localization, and the practicalities of setting up the lab.

    The novel approach taken here banishes determinants to the end of the book and focuses on the central goal of linear algebra: understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space (or an odd-dimensional real vector space) has an eigenvalue. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition includes a new section on orthogonal projections and minimization problems. The sections on self-adjoint operators, normal operators, and the spectral theorem have been rewritten. New examples and new exercises have been added, several proofs have been simplified, and hundreds of minor improvements have been made throughout the text.

    Whatever your circumstances, you can create the life you desire by following simple, counterintuitive steps; not the steps society wants you to follow. Unfortunately, you cannot even trust yourself when it comes to creating the life that you desire, because you’re programmed by your past, an accumulation of hundreds of thousands of years, a mind still adapted for a hunter-gatherer time period. What does this mean? It means every day you’re battling your emotions, logic versus emotion. Logically, you want to do one thing, but emotionally, you are drawn to something else. First, if this sounds familiar, then great, because it means you are human. But, if you’re going to win the game of life, you cannot sit around and wait for things to happen, because you’ll be living a life of supreme comfort and sameness; ultimately, a great sin. You are destined for great things, capable of forging dreams and living your aspirations. Maybe you’ve forgotten about this truth: that you are capable, worthy, and ready. Maybe you’ve been beaten into the ground by life, a teacher who doesn’t care if you’re rich or poor, weak or strong, ready or not, because life will continue forward regardless, waiting for no one. Fortunately, there’re things you can do to set yourself apart from the millions that will ultimately suffer and fail. One such thing, perhaps the most powerful of all, is by studying the great, people who have succeeded and who have gone beyond what is humanly thought possible. When it comes to studying someone great, there’s one name that stands tall, Stephen Hawking, a person who has persevered and flourished. Not only has he conquered great adversity, but he has inspired millions around the world with his brilliant mind. There is a reason why people compare him with the once living Albert Einstein, the genius. What follows are Stephen Hawking’s greatest life lessons, gems of wisdom that you can easily apply to your own life, which will help you live a more abundant, stronger life. If you’re in need of inspiration, a boost of confidence, or just a friendly reminder of the wonders in life, you’ve come to the right place. See for yourself why millions study his work and regard him as a genius. Scroll up and grab your copy today.*** Limited time offer ***

    You can’t fathom the weirdness– unless you’re one of the ER aliens. If you are, buy a dozen copies to give away: to your family, your neighbors, and the PTA. They’ll learn things you never had the heart to tell them, but they should know, like how it's like to work in the ER, the deviousness of shampoo bottles and the dangers of frying bacon naked.

    We nevertheless muddle through even in the absence of theories of how to act. But how do we do it?In Probably Approximately Correct, computer scientist Leslie Valiant presents a masterful synthesis of learning and evolution to show how both individually and collectively we not only survive, but prosper in a world as complex as our own. The key is “probably approximately correct” algorithms, a concept Valiant developed to explain how effective behavior can be learned. The model shows that pragmatically coping with a problem can provide a satisfactory solution in the absence of any theory of the problem. After all, finding a mate does not require a theory of mating. Valiant’s theory reveals the shared computational nature of evolution and learning, and sheds light on perennial questions such as nature versus nurture and the limits of artificial intelligence.Offering a powerful and elegant model that encompasses life’s complexity, Probably Approximately Correct has profound implications for how we think about behavior, cognition, biological evolution, and the possibilities and limits of human and machine intelligence.

    This guidebook is aimed clearly at the needs of the student, with a thorough understanding of, and provision for, the potential conceptual difficulties he or she is likely to encounter.

    It’s already happening in impressive ways. But as we’ve witnessed over the past 20 years, new technology also inevitably raises complex questions and broad societal concerns.” – Brad Smith and Harry Shum on The Future Computed. “As we look to a future powered by a partnership between computers and humans, it’s important that we address these challenges head on. How do we ensure that AI is designed and used responsibly? How do we establish ethical principles to protect people? How should we govern its use? And how will AI impact employment and jobs?” – Brad Smith and Harry Shum on The Future Computed. As Artificial Intelligence shows up in every aspect of our lives, Microsoft's top minds provide a guide discussing how we should prepare for the future. Whether you're a government leader crafting new laws, an entrepreneur looking to incorporate AI into your business, or a parent contemplating the future of education, this book explains the trends driving the AI revolution, identifies the complex ethics and workforce issues we all need to think about and suggests a path forward. Read more: The Future Computed: Artificial Intelligence and its role in society provides Microsoft’s perspective on where AI technology is going and the new societal issues it is raising – ensuring AI is designed and used responsibly, establishing ethical principles to protect people, and how AI will impact employment and jobs. The principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability are critical to addressing the societal impacts of AI and building trust as AI becomes more and more a part of the products and services that people use at work and at home every day. A central theme in The Future Computed is that for AI to deliver on its potential drive widespread economic and social progress, the technology needs to be human-centered – combining the capabilities of computers with human capabilities to enable people to achieve more. But a human-centered approach can only be realized if researchers, policymakers, and leaders from government, business and civil society come together to develop a shared ethical framework for AI. This in turn will help foster responsible development of AI systems that will engender trust. Because in an increasingly AI-driven world the question is not what computers can do, it is what computers should do. The Future Computed also draws a few conclusions as we chart our path forward. First, the companies and countries that will fare best in the AI era will be those that embrace these changes rapidly and effectively. Second, while AI will help solve big societal problems, we must look to this future with a critical eye as there will be challenges as well as opportunities. Third, we need to act with a sense of shared responsibility because AI won’t be created by the tech sector alone. Finally, skilling-up for an AI-powered world involves more than science, technology, engineering and math. As computers behave more like humans, the social sciences and humanities will become grow in importance.

    These efforts evolved into a general theory of expectation that will prove informative to readers interested in cognitive science and evolutionary psychology as well as those interested in music. The book describes a set of psychological mechanisms and illustrates how these mechanisms work in the case of music. All examples of notated music can be heard on the Web.Huron proposes that emotions evoked by expectation involve five functionally distinct response systems: reaction responses (which engage defensive reflexes); tension responses (where uncertainty leads to stress); prediction responses (which reward accurate prediction); imagination responses (which facilitate deferred gratification); and appraisal responses (which occur after conscious thought is engaged). For real-world events, these five response systems typically produce a complex mixture of feelings. The book identifies some of the aesthetic possibilities afforded by expectation, and shows how common musical devices (such as syncopation, cadence, meter, tonality, and climax) exploit the psychological opportunities. The theory also provides new insights into the physiological psychology of awe, laughter, and spine-tingling chills. Huron traces the psychology of expectations from the patterns of the physical/cultural world through imperfectly learned heuristics used to predict that world to the phenomenal qualia we experienced as we apprehend the world.

    The victory of IBM's Watson on the television quiz show Jeopardy! revealed how scientists and engineers at IBM and elsewhere are pushing the boundaries of science and technology to create machines that sense, learn, reason, and interact with people in new ways to provide insight and advice.In Smart Machines, John E. Kelly III, director of IBM Research, and Steve Hamm, a writer at IBM and a former business and technology journalist, introduce the fascinating world of "cognitive systems" to general audiences and provide a window into the future of computing. Cognitive systems promise to penetrate complexity and assist people and organizations in better decision making. They can help doctors evaluate and treat patients, augment the ways we see, anticipate major weather events, and contribute to smarter urban planning. Kelly and Hamm's comprehensive perspective describes this technology inside and out and explains how it will help us conquer the harnessing and understanding of "big data," one of the major computing challenges facing businesses and governments in the coming decades. Absorbing and impassioned, their book will inspire governments, academics, and the global tech industry to work together to power this exciting wave in innovation.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    With a new introduction, three new chapters, modernized language and methods throughout, and an appendix of challenging and enjoyable practice problems, Calculus Made Easy has been thoroughly updated for the modern reader.

    In 2007, Janet Belsky's "Experiencing the Lifespan" was published to widespread instructor and student acclaim, ultimately winning the 2008 Textbook Excellence Award from the Text and Academic Authors Association. Now that breakthrough text returns in a rigorously updated edition that explores the lifespan by combining the latest research with a practicing psychologist's understanding of people, and a teacher's understanding of students and classroom dynamics. And again, all of this in the right number of pages to fit comfortably in a single term course.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."