For the first time, these books are available as a boxed, three-volume set. The handsome slipcase makes this set an ideal gift for the recent computer science graduate or professional programmer. Offering a description of classical computer science, this multi-volume work is a useful resource in programming theory and practice for students, researchers, and practitioners alike. For programmers, it offers cookbook solutions to their day-to-day problems.

    In 1996 Joseph LeDoux's "The Emotional Brain" presented a revelatory examination of the biological bases of our emotions and memories. Now, the world-renowned expert on the brain has produced with a groundbreaking work that tells a more profound story: how the little spaces between the neurons-the brain's synapses--are the channels through which we think, act, imagine, feel, and remember. Synapses encode the essence of personality, enabling each of us to function as a distinctive, integrated individual from moment to moment. Exploring the functioning of memory, the synaptic basis of mental illness and drug addiction, and the mechanism of self-awareness, "Synaptic Self" is a provocative and mind-expanding work that is destined to become a classic.

    It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.

    The book begins with the basic concepts of logic and set theory, to familiarize students with the language of mathematics and how it is interpreted. These concepts are used as the basis for a step-by-step breakdown of the most important techniques used in constructing proofs. To help students construct their own proofs, this new edition contains over 200 new exercises, selected solutions, and an introduction to Proof Designer software. No background beyond standard high school mathematics is assumed. Previous Edition Hb (1994) 0-521-44116-1 Previous Edition Pb (1994) 0-521-44663-5

    In this virtuosic work of popular science, neuroscientist and best-selling author Dean Buonomano investigates the intricate relationship between the brain and time: What is time? Why does time seem to speed up or slow down? Is our sense that time flows an illusion? Buonomano presents his own influential theory of how the brain tells time, and he illuminates such concepts as free will, consciousness, spacetime, and relativity from the perspective of a neuroscientist. Drawing on physics, evolutionary biology, and philosophy, Your Brain Is a Time Machine reveals that the brain’s ultimate purpose may be to predict the future, and thus that your brain is a time machine.

    

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    This comprehensive work covers both statistical and symbolic approaches to language processing; it shows how they can be applied to important tasks such as speech recognition, spelling and grammar correction, information extraction, search engines, machine translation, and the creation of spoken-language dialog agents. The following distinguishing features make the text both an introduction to the field and an advanced reference guide.- UNIFIED AND COMPREHENSIVE COVERAGE OF THE FIELDCovers the fundamental algorithms of each field, whether proposed for spoken or written language, whether logical or statistical in origin.- EMPHASIS ON WEB AND OTHER PRACTICAL APPLICATIONSGives readers an understanding of how language-related algorithms can be applied to important real-world problems.- EMPHASIS ON SCIENTIFIC EVALUATIONOffers a description of how systems are evaluated with each problem domain.- EMPERICIST/STATISTICAL/MACHINE LEARNING APPROACHES TO LANGUAGE PROCESSINGCovers all the new statistical approaches, while still completely covering the earlier more structured and rule-based methods.

    From his intimate knowledge of S., the mnemonist, gained from conversations and testing over a period of almost thirty years, A. R. Luria is able to reveal in rich detail not only the obvious strengths of S.’s astonishing memory but also his surprising weaknesses: his crippling inability to forget, his pattern of reacting passively to life, and his uniquely handicapped personality.

    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."

    Unlike traditional empirical psychology, it is more oriented toward the how than the what. An experiment in mind design is more likely to be an attempt to build something and make it work--as in artificial intelligence--than to observe or analyze what already exists. Mind design is psychology by reverse engineering.When Mind Design was first published in 1981, it became a classic in the then-nascent fields of cognitive science and AI. This second edition retains four landmark essays from the first, adding to them one earlier milestone (Turing's Computing Machinery and Intelligence) and eleven more recent articles about connectionism, dynamical systems, and symbolic versus nonsymbolic models. The contributors are divided about evenly between philosophers and scientists. Yet all are philosophical in that they address fundamental issues and concepts; and all are scientific in that they are technically sophisticated and concerned with concrete empirical research.ContributorsRodney A. Brooks, Paul M. Churchland, Andy Clark, Daniel C. Dennett, Hubert L. Dreyfus, Jerry A. Fodor, Joseph Garon, John Haugeland, Marvin Minsky, Allen Newell, Zenon W. Pylyshyn, William Ramsey, Jay F. Rosenberg, David E. Rumelhart, John R. Searle, Herbert A. Simon, Paul Smolensky, Stephen Stich, A.M. Turing, Timothy van Gelder

    Republished in book form shortly thereafter, it has since gone through four hardcover and sixteen paperback printings. It is a revolutionary work, astounding in its foresight and contemporaneity. The University of Illinois Press is pleased and honored to issue this commemorative reprinting of a classic.

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.