This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    Their pioneering work, presented here in an elegant style, challenges much of the conventional wisdom about consciousness. The Universe of Consciousness has enormous implications for our understanding of language, thought, emotion, and mental illness.

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

    After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layerperceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

    In Self Comes to Mind, he goes against the long-standing idea that consciousness is somehow separate from the body, presenting compelling new scientific evidence that consciousness—what we think of as a mind with a self—is to begin with a biological process created by a living organism. Besides the three traditional perspectives used to study the mind (the introspective, the behavioral, and the neurological), Damasio introduces an evolutionary perspective that entails a radical change in the way the history of conscious minds is viewed and told. He also advances a radical hypothesis regarding the origins and varieties of feelings, which is central to his framework for the biological construction of consciousness: feelings are grounded in a near fusion of body and brain networks, and first emerge from the historically old and humble brain stem rather than from the modern cerebral cortex.  Damasio suggests that the brain’s development of a human self becomes a challenge to nature’s indifference and opens the way for the appearance of culture, a radical break in the course of evolution and the source of a new level of life regulation—sociocultural homeostasis. He leaves no doubt that the blueprint for the work-in-progress he calls sociocultural homeostasis is the genetically well-established basic homeostasis, the curator of value that has been present in simple life-forms for billions of years. Self Comes to Mind is a groundbreaking journey into the neurobiological foundations of mind and self.Downloadhttp://depositfiles.com/files/xlt08paxhOrhttp://www.filesonic.com/file/3554828...

    Ramachandran is internationally renowned for uncovering answers to the deep and quirky questions of human nature that few scientists have dared to address. His bold insights about the brain are matched only by the stunning simplicity of his experiments -- using such low-tech tools as cotton swabs, glasses of water and dime-store mirrors. In Phantoms in the Brain, Dr. Ramachandran recounts how his work with patients who have bizarre neurological disorders has shed new light on the deep architecture of the brain, and what these findings tell us about who we are, how we construct our body image, why we laugh or become depressed, why we may believe in God, how we make decisions, deceive ourselves and dream, perhaps even why we're so clever at philosophy, music and art. Some of his most notable cases:A woman paralyzed on the left side of her body who believes she is lifting a tray of drinks with both hands offers a unique opportunity to test Freud's theory of denial.A man who insists he is talking with God challenges us to ask: Could we be "wired" for religious experience?A woman who hallucinates cartoon characters illustrates how, in a sense, we are all hallucinating, all the time.Dr. Ramachandran's inspired medical detective work pushes the boundaries of medicine's last great frontier -- the human mind -- yielding new and provocative insights into the "big questions" about consciousness and the self.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.

    He studied physics and philosophy at the University of Tübingen in Germany and was awarded his Ph.D. in biophysics in 1982. He is now the Lois and Victor Troendle Professor of Cognitive and Behavioral Biology at the California Institute of Technology. The author of several books, Dr. Koch studies the biophysics of computation, and the neuronal basis of visual perception, attention, and consciousness. Together with Francis Crick, his long-time collaborator, he has pioneered the scientific study of consciousness.

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

    Its greatest detriment to the world has been its unfettered desire to play with and alter them: science for science's sake, as if it offered the only path to knowledge.According to Lehrer, when it comes to the human brain, the world of art unraveled such mysteries long before the neuroscientists: "This book is about artists who anticipated the discoveries of science who discovered truths about the human mind that science is only now discovering." 'Proust Was a Neuroscientist' is a dazzling inquiry into the nature of the mind and of the truths harvested by its first explorers: artists like Walt Whitman, George Eliot, Auguste Escoffier, Marcel Proust, Paul Cozanne, Igor Stravinsky, Gertrude Stein, and Virginia Woolf. What they understood intuitively and expressed through their respective art forms -- the fallibility of memory, the malleability of the brain, the subtleties of vision, and the deep structure of language -- science has only now begun to measure and confirm. Blending biography, criticism, and science writing, Lehrer offers a lucid examination of eight artistic thinkers who lit the path toward a greater understanding of the human mind and a deeper appreciation of the ineffable mystery of life.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse engineering the brain to understand precisely how it works and using that knowledge to create even more intelligent machines.Kurzweil discusses how the brain functions, how the mind emerges from the brain, and the implications of vastly increasing the powers of our intelligence in addressing the world’s problems. He thoughtfully examines emotional and moral intelligence and the origins of consciousness and envisions the radical possibilities of our merging with the intelligent technology we are creating.Certain to be one of the most widely discussed and debated science books of the year, How to Create a Mind is sure to take its place alongside Kurzweil’s previous classics which include Fantastic Voyage: Live Long Enough to Live Forever and The Age of Spiritual Machines.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.