Fundamentals of Biostatistics with CD-Rom.

    Following an introduction to neural structure and function, all the key methods and procedures of cognitive neuroscience are explained, with a view to helping students understand how they can be used to shed light on the neural basis of cognition.The second part of the book goes on to present an up-to-date overview of the latest theories and findings in all the key topics in cognitive neuroscience, including vision, attention, memory, speech and language, numeracy, executive function and social and emotional behaviour. Throughout, case studies, newspaper reports and everyday examples are used to provide an easy way in to understanding the more challenging ideas that underpin the subject.In addition each chapter includes:Summaries of key terms and points Example essay questions to aid exam preparation Recommended further reading Feature boxes exploring interesting and popular questions and their implications for the subject.Written in an engaging style by a leading researcher in the field, this book will be invaluable as a core text for undergraduate modules in cognitive neuroscience. It can also be used as a key text on courses in cognition, cognitive neuropsychology or brain and behaviour. Those embarking on research will find it an invaluable starting point and reference.We offer CD-ROM-based resources free of charge to instructors who recommend The Student's Guide to Cognitive Neuroscience by Jamie Ward. These resources include:A chapter-by-chapter, illustrated slideshow lecture course An innovative bank of multiple-choice questions, graded according to difficulty and which allow for confidence-weighted answers Comprehensive lecture planning advice tailored to different length courses.Jamie Ward has researched and taught extensively in many areas of cognitive neuroscience. He is a leading authority on the subject of synaesthesia and has contributed to a wider understanding of it in both academic and lay circles.

    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.

    While the mathematics is there, the effort is not all concentrated on proofs. Strang's emphasis is on understanding. He explains concepts, rather than deduces. This book is written in an informal and personal style and teaches real mathematics. The gears change in Chapter 2 as students reach the introduction of vector spaces. Throughout the book, the theory is motivated and reinforced by genuine applications, allowing pure mathematicians to teach applied mathematics.

    The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.

    This Eighth Edition redefines the high standard set by previous editions. It offers the best balance of rigor and accessibility, the most current research, and the most thorough technology integration available for your course--all presented within a unique modular format that supports student mastery and provides instructors with maximum teaching flexibility. In every chapter, Kalat accurately portrays biopsychology as a dynamic and empirical field in which fascinating new discoveries are constantly being made. He captures readers' interest with the latest biological psychology findings, such as how gingko biloba claims to aid memory and coverage of the hypothesis that humans' mate choice patterns are influenced by natural selection. Throughout, the author's goal is not only to convey information, but also to convey his excitement about and dedication to the subject.

    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.

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

    This sense of our self may seem incredibly real but a wealth of recent scientific evidence reveals that it is not what it seems--it is all an illusion. In The Self Illusion, Bruce Hood reveals how the self emerges during childhood and how the architecture of the developing brain enables us to become social animals dependent on each other. Humans spend proportionally the greatest amount of time in childhood compared to any other animal. It's not only to learn from others, Hood notes, but also to learn to become like others. We learn to become our self. Even as adults we are continually developing and elaborating this story, learning to become different selves in different situations--the work self, the home self, the parent self. Moreover, Hood shows that this already fluid process--the construction of self--has dramatically changed in recent years. Social networking activities--such as blogging, Facebook, LinkedIn, and Twitter--are fast becoming socialization on steroids. The speed and ease at which we can form alliances and relationships are outstripping the same selection processes that shaped our self prior to the internet era. Things will never be the same again in the online social world. Hood offers our first glimpse into this unchartered territory. Who we are is, in short, a story of our self--a narrative that our brain creates. Like the science fiction movie, we are living in a matrix that is our mind. But Hood concludes that though the self is an illusion, it is an illusion we must continue to embrace to live happily in human society.

    With this long-awaited revision, the authors continue to present the theory in a concise and straightforward manner, now with an eye out for the practical applications. They have revised this book to make it more accessible to today's students, including the addition of more material on writing proofs, more figures and pictures to convey ideas, side-boxes to highlight other interesting material, and a less formal writing style. Exercises at the end of each chapter, including some new, easier exercises, help readers confirm and enhance their understanding of the material. *NEW! Completely rewritten to be less formal, providing more accessibility to todays students. *NEW! Increased usage of figures and pictures to help convey ideas. *NEW! More detail and intuition provided for definitions and proofs. *NEW! Provides special side-boxes to present supplemental material that may be of interest to readers. *NEW! Includes more exercises, including many at a lower level. *NEW! Presents program-like notation for PDAs and Turing machines. *NEW! Increas

    The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo MCMC. Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

    It is a ‘ must’ book for those in every branch of science . . . in addition, economists, politicians, statesmen, and businessmen cannot afford to overlook cybernetics and its tremendous, even terrifying implications. "It is a beautifully written book, lucid, direct, and despite its complexity, as readable by the layman as the trained scientist." -- John B. Thurston, "The Saturday Review of Literature" Acclaimed one of the "seminal books . . . comparable in ultimate importance to . . . Galileo or Malthus or Rousseau or Mill," "Cybernetics" was judged by twenty-seven historians, economists, educators, and philosophers to be one of those books published during the "past four decades", which may have a substantial impact on public thought and action in the years ahead." -- Saturday Review

    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.

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

      Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.