This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    Dennett, Gerald Edelman, and Roger Penrose, all firing volleys in what has come to be called the consciousness wars. Now, in The Conscious Mind, philosopher David J. Chalmers offers a cogent analysis of this heated debate as he unveils a major new theory of consciousness, one that rejects the prevailing reductionist trend of science, while offering provocative insights into the relationship between mind and brain.Writing in a rigorous, thought-provoking style, the author takes us on a far-reaching tour through the philosophical ramifications of consciousness. Chalmers convincingly reveals how contemporary cognitive science and neurobiology have failed to explain how and why mental events emerge from physiological occurrences in the brain. He proposes instead that conscious experience must be understood in an entirely new light--as an irreducible entity (similar to such physical properties as time, mass, and space) that exists at a fundamental level and cannot be understood as the sum of its parts. And after suggesting some intriguing possibilities about the structure and laws of conscious experience, he details how his unique reinterpretation of the mind could be the focus of a new science. Throughout the book, Chalmers provides fascinating thought experiments that trenchantly illustrate his ideas. For example, in exploring the notion that consciousness could be experienced by machines as well as humans, Chalmers asks us to imagine a thinking brain in which neurons are slowly replaced by silicon chips that precisely duplicate their functions--as the neurons are replaced, will consciousness gradually fade away? The book also features thoughtful discussions of how the author's theories might be practically applied to subjects as diverse as artificial intelligence and the interpretation of quantum mechanics.All of us have pondered the nature and meaning of consciousness. Engaging and penetrating, The Conscious Mind adds a fresh new perspective to the subject that is sure to spark debate about our understanding of the mind for years to come.

    We can actually observe a person's brain registering a joke or experiencing a painful memory. Drawing on the latest imaging technology and the expertise of distinguished scientists, Rita Carter explores the geography of the human brain. Her writing is clear, accessible, witty, and the book's 150 illustrations—most in color—present an illustrated guide to that wondrous, coconut-sized, wrinkled gray mass we carry inside our heads.Mapping the Mind charts the way human behavior and culture have been molded by the landscape of the brain. Carter shows how our personalities reflect the biological mechanisms underlying thought and emotion and how behavioral eccentricities may be traced to abnormalities in an individual brain. Obsessions and compulsions seem to be caused by a stuck neural switch in a region that monitors the environment for danger. Addictions stem from dysfunction in the brain's reward system. Even the sense of religious experience has been linked to activity in a certain brain region. The differences between men and women's brains, the question of a "gay brain," and conditions such as dyslexia, autism, and mania are also explored.Looking inside the brain, writes Carter, we see that actions follow from our perceptions, which are due to brain activity dictated by a neuronal structure formed from the interplay between our genes and the environment. Without sidestepping the question of free will, Carter suggests that future generations will use our increasing knowledge of the brain to "enhance those mental qualities that give sweetness and meaning to our lives, and to eradicate those that are destructive."

    With deft use of examples of humor and wordplay, Steven Pinker weaves our vast knowledge of language into a compelling story: language is a human instinct, wired into our brains by evolution. The Language Instinct received the William James Book Prize from the American Psychological Association and the Public Interest Award from the Linguistics Society of America. This edition includes an update on advances in the science of language since The Language Instinct was first published.

    Explaining why the whole is sometimes smarter than the sum of its parts, Johnson presents surprising examples of feedback, self-organization, and adaptive learning. How does a lively neighborhood evolve out of a disconnected group of shopkeepers, bartenders, and real estate developers? How does a media event take on a life of its own? How will new software programs create an intelligent World Wide Web? In the coming years, the power of self-organization -- coupled with the connective technology of the Internet -- will usher in a revolution every bit as significant as the introduction of electricity. Provocative and engaging, Emergence puts you on the front lines of this exciting upheaval in science and thought.

    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.

    Scientists have long supported this assumption by claiming that emotions are hardwired in the body or the brain. Today, however, the science of emotion is in the midst of a revolution on par with the discovery of relativity in physics and natural selection in biology—ans this paradigm shift has far-reaching implications for us all.Leading the charge is psychologist and neuroscientist Lisa Feldman Barrett, whose theory of emotion is driving a deeper understanding of the mind and brain, and shedding new light on what it means to be human. Her research overturns the widely held belief that emotions are housed in different parts of the brain and are universally expressed and recognized. Instead, she has shown that emotion is constructed in the moment, by core systems that interact across the whole brain, aided by a lifetime of learning. This new theory means that you play a much greater role in your emotional life than you ever thought. Its repercussions are already shaking the foundations not only of psychology but also of medicine, the legal system, child-rearing, meditation, and even airport security.Why do emotions feel automatic? Does rational thought really control emotion? How does emotion affect disease? How can you make your children more emotionally intelligent? How Emotions Are Made answers these questions and many more, revealing the latest research and intriguing practical applications of the new science of emotion, mind, and brain.

    Beautiful Evidence is about how seeing turns into showing, how data and evidence turn into explanation. The book identifies excellent and effective methods for showing nearly every kind of information, suggests many new designs (including sparklines), and provides analytical tools for assessing the credibility of evidence presentations (which are seen from both sides: how to produce and how to consume presentations). For alert consumers of presentations, there are chapters on diagnosing evidence corruption and PowerPoint pitches. Beautiful Evidence concludes with two chapters that leave the world of pixel and paper flatland representations - and move onto seeing and thinking in space land, the real-land of three-space and time.

    It argues that evolution designed us both to teach and learn, and that the drive to learn is our most important instinct. It also reveals as fascinating insights about our adult capacities and how even young children -- as well as adults -- use some of the same methods that allow scientists to learn so much about the world. Filled with surprise at every turn, this vivid, lucid, and often funny book gives us a new view of the inner life of children and the mysteries of the mind.

    This edition provides increased coverage of taste and smell, circadian rhythms, brain development, and developmental disorders and includes new information on molecular mechanisms and functional brain imaging. Path of Discovery boxes, written by leading researchers, highlight major current discoveries. In addition, readers will be able to assess their knowledge of neuroanatomy with the Illustrated Guide to Human Neuroanatomy, which includes a perforated self-testing workbook.This edition's robust ancillary package includes a bound-in student CD-ROM, an Instructor's Resource CD-ROM, and resources online.

    If reason is so useful, why didn't it also evolve in other animals? If reason is that reliable, why do we produce so much thoroughly reasoned nonsense? In their groundbreaking account of the evolution and workings of reason, Hugo Mercier and Dan Sperber set out to solve this double enigma. Reason, they argue with a compelling mix of real-life and experimental evidence, is not geared to solitary use, to arriving at better beliefs and decisions on our own. What reason does, rather, is help us justify our beliefs and actions to others, convince them through argumentation, and evaluate the justifications and arguments that others address to us.In other words, reason helps humans better exploit their uniquely rich social environment. This interactionist interpretation explains why reason may have evolved and how it fits with other cognitive mechanisms. It makes sense of strengths and weaknesses that have long puzzled philosophers and psychologists--why reason is biased in favor of what we already believe, why it may lead to terrible ideas and yet is indispensable to spreading good ones.Ambitious, provocative, and entertaining, The Enigma of Reason will spark debate among psychologists and philosophers, and make many reasonable people rethink their own thinking.

    In pursuit of these things, it is undeterred by emotion, fear, or morality. Dopamine is the source of our every urge, that little bit of biology that makes an ambitious business professional sacrifice everything in pursuit of success, or that drives a satisfied spouse to risk it all for the thrill of someone new. Simply put, it is why we seek and succeed; it is why we discover and prosper. Yet, at the same time, it’s why we gamble and squander. From dopamine’s point of view, it’s not the having that matters. It’s getting something—anything—that’s new. From this understanding—the difference between possessing something versus anticipating it—we can understand in a revolutionary new way why we behave as we do in love, business, addiction, politics, religion – and we can even predict those behaviors in ourselves and others. In The Molecule of More: How a Single Chemical in Your Brain Drives Love, Sex, and Creativity—and will Determine the Fate of the Human Race, George Washington University professor and psychiatrist Daniel Z. Lieberman, MD, and Georgetown University lecturer Michael E. Long present a potentially life-changing proposal: Much of human life has an unconsidered component that explains an array of behaviors previously thought to be unrelated, including why winners cheat, why geniuses often suffer with mental illness, why nearly all diets fail, and why the brains of liberals and conservatives really are different.

    Full of insights, arguments and philosophical perspectives, the book covers an amazing array of topics. Beginning in antiquity with Democritus, it progresses through logic and set theory, computability and complexity theory, quantum computing, cryptography, the information content of quantum states and the interpretation of quantum mechanics. There are also extended discussions about time travel, Newcomb's Paradox, the anthropic principle and the views of Roger Penrose. Aaronson's informal style makes this fascinating book accessible to readers with scientific backgrounds, as well as students and researchers working in physics, computer science, mathematics and philosophy.

    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

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.