What is it like to try to heal the body when the mind is under attack? In this book, Dr. Allan Ropper and Brian Burrell take the reader behind the scenes at Harvard Medical School's neurology unit to show how a seasoned diagnostician faces down bizarre, life-altering afflictions. Like Alice in Wonderland, Dr. Ropper inhabits a world where absurdities abound:• A figure skater whose body has become a ticking time-bomb • A salesman who drives around and around a traffic rotary, unable to get off • A college quarterback who can't stop calling the same play • A child molester who, after falling on the ice, is left with a brain that is very much dead inside a body that is very much alive • A mother of two young girls, diagnosed with ALS, who has to decide whether a life locked inside her own head is worth livingHow does one begin to treat such cases, to counsel people whose lives may be changed forever? How does one train the next generation of clinicians to deal with the moral and medical aspects of brain disease? Dr. Ropper and his colleague answer these questions by taking the reader into a rarified world where lives and minds hang in the balance.

    Too often, overwhelmed by anatomical detail, students miss out on the functional beauty of the nervous system and its relevance to clinical practice.

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    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.

    Included are step-by-step instructions on how to carry out Bayesian data analyses.Download Link : readbux.com/download?i=0124058884            0124058884 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan PDF by John Kruschke

    This work represents the views of a mathematician on the analogies between computing machines and the living human brain.

    This book teaches basic Artificial Intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series, but are very useful in their own right. The book explains all algorithms using actual numeric calculations that you can perform yourself. Artificial Intelligence for Humans is a book series meant to teach AI to those without an extensive mathematical background. The reader needs only a knowledge of basic college algebra or computer programming—anything more complicated than that is thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, R, Python and C. Other languages planned.

    Even modern neuroscience has tended, until recently, to concentrate on the cognitive aspects of brain function, disregarding emotions. This attitude began to change with the publication of Descartes’ Error in 1995. Antonio Damasio—"one of the world’s leading neurologists" (The New York Times)—challenged traditional ideas about the connection between emotions and rationality. In this wondrously engaging book, Damasio takes the reader on a journey of scientific discovery through a series of case studies, demonstrating what many of us have long suspected: emotions are not a luxury, they are essential to rational thinking and to normal social behavior.

    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.

    A computational investigation into the human representation and processing of visual information.

    Admittedly, computers now play chess at the grandmaster level, but do they understand the game as we do? Can a computer eventually do everything a human mind can do? In this absorbing and frequently contentious book, Roger Penrose--eminent physicist and winner, with Stephen Hawking, of the prestigious Wolf prize--puts forward his view that there are some facets of human thinking that can never be emulated by a machine. Penrose examines what physics and mathematics can tell us about how the mind works, what they can't, and what we need to know to understand the physical processes of consciousness. He is among a growing number of physicists who think Einstein wasn't being stubborn when he said his little finger told him that quantum mechanics is incomplete, and he concludes that laws even deeper than quantum mechanics are essential for the operation of a mind. To support this contention, Penrose takes the reader on a dazzling tour that covers such topics as complex numbers, Turing machines, complexity theory, quantum mechanics, formal systems, Godel undecidability, phase spaces, Hilbert spaces, black holes, white holes, Hawking radiation, entropy, quasicrystals, the structure of the brain, and scores of other subjects. The Emperor's New Mind will appeal to anyone with a serious interest in modern physics and its relation to philosophical issues, as well as to physicists, mathematicians, philosophers and those on either side of the AI debate.

    Consciousness: A Very Short Introduction challenges readers to reconsider key concepts such as personality, free will, and the soul. How can a physical brain create our experience of the world? What creates our identity? Do we really have free will? Could consciousness itself be an illusion? Exciting new developments in brain science are opening up these debates, and the field has now expanded to include biologists, neuroscientists, psychologists, and philosophers. This book clarifies the potentially confusing arguments and clearly describes the major theories, with illustrations and lively cartoons to help explain the experiments. Topics include vision and attention, theories of self, experiments on action and awareness, altered states of consciousness, and the effects of brain damage and drugs. This lively, engaging, and authoritative book provides a clear overview of the subject that combines the perspectives of philosophy, psychology, and neuroscience--and serves as a much-needed launch pad for further exploration of this complicated and unsolved issue.About the Series: Combining authority with wit, accessibility, and style, Very Short Introductions offer an introduction to some of life's most interesting topics. Written by experts for the newcomer, they demonstrate the finest contemporary thinking about the central problems and issues in hundreds of key topics, from philosophy to Freud, quantum theory to Islam.

    Patel challenges the widespread belief that music and language are processed independently. Since Plato's time, the relationship between music and language has attracted interest and debate from a wide range of thinkers. Recently, scientific research on this topic has been growing rapidly, as scholars from diverse disciplines, including linguistics, cognitive science, music cognition, and neuroscience are drawn to the music-language interface as one way to explore the extent to which different mental abilities are processed by separate brain mechanisms. Accordingly, the relevant data and theories have been spread across a range of disciplines. This volume provides the first synthesis, arguing that music and language share deep and critical connections, and that comparative research provides a powerful way to study the cognitive and neural mechanisms underlying these uniquely human abilities.Winner of the 2008 ASCAP Deems Taylor Award.

    Cognitive Science draws upon many academic disciplines, including Psychology, Computer Science, Philosophy, Linguistics and Neuroscience. This is the first textbook to present a unified view of Cognitive Science as a discipline in its own right, with a distinctive approach to studying the mind. Students are introduced to the cognitive scientist's 'toolkit' - the vast range of techniques and tools that cognitive scientists can use to study the mind. The book presents the main theoretical models that cognitive scientists are currently using, and shows how those models are being applied to unlock the mysteries of the human mind. Cognitive Science is replete with examples, illustrations, and applications, and draws on cutting-edge research and new developments to explore both the achievements that cognitive scientists have made, and the challenges that lie ahead.

    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.