The way a teaching machine works is: It asks you a question. If you give the right answer, it goes on to the next question. If you give the wrong answer, it tells you why the answer is wrong and tells you to go back and try again. This is called "programmed learning". The real authors were experts and authorities in the field of programmed learning. Bobby Fischer lent his name to the project. Stuart Margulies is a chess master and also a recognized authority on programmed learning. He is a widely published author of more than 40 books, all in the field of programmed learning, especially in learning how to read. For example, one of his books is "Critical reading for proficiency 1 : introductory level". Donn Mosenfelder is not a known or recognized chess player, but he was the owner of the company that developed and designed this book. He has written more than 25 books, almost all on basic reading, writing and math.

    In this smart and funny fan’s guide Hample explains the ins and outs of pitching, hitting, running, and fielding, while offering insider trivia and anecdotes that will surprise even the most informed viewers of our national pastime.What is the difference between a slider and a curveball?At which stadium did “The Wave” first make an appearance? How do some hitters use iPods to improve their skills?Which positions are never played by lefties?Why do some players urinate on their hands?Combining the narrative voice and attitude of Michael Lewis with the compulsive brilliance of Schott’s Miscellany, Watching Baseball Smarter will increase your understanding and enjoyment of the sport–no matter what your level of expertise. Zack Hample is an obsessed fan and a regular writer for minorleaguebaseball.com. He's collected nearly 3,000 baseballs from major league games and has appeared on dozens of TV and radio shows. His first book, How to Snag Major League Baseballs, was published in 1999.

    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.

    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.

    But in the past fifteen years, a revolutionary new standard of measurement—sabermetrics—has been embraced by front offices in Major League Baseball and among fantasy baseball enthusiasts. But while sabermetrics is recognized as being smarter and more accurate, traditionalists, including journalists, fans, and managers, stubbornly believe that the "old" way—a combination of outdated numbers and "gut" instinct—is still the best way. Baseball, they argue, should be run by people, not by numbers.?In this informative and provocative book, teh renowned ESPN analyst and senior baseball writer demolishes a century’s worth of accepted wisdom, making the definitive case against the long-established view. Armed with concrete examples from different eras of baseball history, logic, a little math, and lively commentary, he shows how the allegiance to these numbers—dating back to the beginning of the professional game—is firmly rooted not in accuracy or success, but in baseball’s irrational adherence to tradition.While Law gores sacred cows, from clutch performers to RBIs to the infamous save rule, he also demystifies sabermetrics, explaining what these "new" numbers really are and why they’re vital. He also considers the game’s future, examining how teams are using Data—from PhDs to sophisticated statistical databases—to build future rosters; changes that will transform baseball and all of professional sports.

    In writing the book James Stewart asked himself: What is essential for a three-semester calculus course for scientists and engineers? Stewart's ESSENTIAL CALCULUS offers a concise approach to teaching calculus that focuses on major concepts and supports those concepts with precise definitions, patient explanations, and carefully graded problems. Essential Calculus is only 850 pages-two-thirds the size of Stewart's other calculus texts (CALCULUS, Fifth Edition and CALCULUS, EARLY TRANSCENDENTALS, Fifth Edition)-and yet it contains almost all of the same topics. The author achieved this relative brevity mainly by condensing the exposition and by putting some of the features on the website, www.StewartCalculus.com. Despite the reduced size of the book, there is still a modern flavor: Conceptual understanding and technology are not neglected, though they are not as prominent as in Stewart's other books. ESSENTIAL CALCULUS has been written with the same attention to detail, eye for innovation, and meticulous accuracy that have made Stewart's textbooks the best-selling calculus texts in the world.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    

    In Turing’s Cathedral, George Dyson focuses on a small group of men and women, led by John von Neumann at the Institute for Advanced Study in Princeton, New Jersey, who built one of the first computers to realize Alan Turing’s vision of a Universal Machine. Their work would break the distinction between numbers that mean things and numbers that do things—and our universe would never be the same. Using five kilobytes of memory (the amount allocated to displaying the cursor on a computer desktop of today), they achieved unprecedented success in both weather prediction and nuclear weapons design, while tackling, in their spare time, problems ranging from the evolution of viruses to the evolution of stars. Dyson’s account, both historic and prophetic, sheds important new light on how the digital universe exploded in the aftermath of World War II. The proliferation of both codes and machines was paralleled by two historic developments: the decoding of self-replicating sequences in biology and the invention of the hydrogen bomb. It’s no coincidence that the most destructive and the most constructive of human inventions appeared at exactly the same time.  How did code take over the world? In retracing how Alan Turing’s one-dimensional model became John von Neumann’s two-dimensional implementation, Turing’s Cathedral offers a series of provocative suggestions as to where the digital universe, now fully three-dimensional, may be heading next.

    In the beginning was Josh Levine, an idealistic programming genius who dreamed of wresting control of the market from the big exchanges that, again and again, gave the giant institutions an advantage over the little guy. Levine created a computerized trading hub named Island where small traders swapped stocks, and over time his invention morphed into a global electronic stock market that sent trillions in capital through a vast jungle of fiber-optic cables. By then, the market that Levine had sought to fix had turned upside down, birthing secretive exchanges called dark pools and a new species of trading machines that could think, and that seemed, ominously, to be slipping the control of their human masters. Dark Pools is the fascinating story of how global markets have been hijacked by trading robots--many so self-directed that humans can't predict what they'll do next.

    Trading Bases explains how he did it. After the fall of Lehman Brothers, Joe Peta was out of a job. He found a new one but lost that, too, when an ambulance mowed him down. In search of a way to cheer himself up while he recuperated in a wheelchair, Peta started watching baseball again, as he had growing up. That’s when inspiration hit: Why not apply his outstanding risk-analysis skills to improve on sabermetrics, the method made famous by Moneyball—and beat the only market in town, the Vegas betting line? Why not treat MLB like the S&P 500? In Trading Bases, Peta shows how to subtract luck—in particular “cluster luck,” as he puts it—from a team’s statistics to best predict how it will perform in the next game and over the whole season. His baseball “hedge fund” returned an astounding 41 percent in 2011—and has never been down more than 5 percent. Peta takes readers to the ballpark in San Francisco, trading floors and baseball bars in New York, and sports books in Vegas, all while tracing the progress of his wagers. Often humorous, occasionally touching, and with a wink toward the sheer implausibility of the whole project, Trading Bases is all about the love of critical reasoning, trading cultures, risk management, and baseball. And not necessarily in that order.

    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.

    No statistical knowledge is required, and your programming skills can range from hobbyist to pro.Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: Create artful graphs to visualize complex data sets and functions Write more efficient code using parallel R and vectorization Interface R with C/C++ and Python for increased speed or functionality Find new R packages for text analysis, image manipulation, and more Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

    Nobody is perfect. And that includes five of the greatest scientists in history—Charles Darwin, William Thomson (Lord Kelvin), Linus Pauling, Fred Hoyle, and Albert Einstein. But the mistakes that these great luminaries made helped advance science. Indeed, as Mario Livio explains, science thrives on error, advancing when erroneous ideas are disproven.As a young scientist, Einstein tried to conceive of a way to describe the evolution of the universe at large, based on General Relativity—his theory of space, time, and gravity. Unfortunately he fell victim to a misguided notion of aesthetic simplicity. Fred Hoyle was an eminent astrophysicist who ridiculed an emerging theory about the origin of the universe that he dismissively called “The Big Bang.” The name stuck, but Hoyle was dead wrong in his opposition.They, along with Darwin (a blunder in his theory of Natural Selection), Kelvin (a blunder in his calculation of the age of the earth), and Pauling (a blunder in his model for the structure of the DNA molecule), were brilliant men and fascinating human beings. Their blunders were a necessary part of the scientific process. Collectively they helped to dramatically further our knowledge of the evolution of life, the Earth, and the universe.

    Whether someone is trying to qualify for the Olympics, break ground in mathematical theory or craft an artistic masterpiece, many of the practices that lead to great success are the same. In Peak Performance, Brad Stulberg, a former McKinsey and Company consultant and journalist who covers health and the science of human performance, and Steve Magness, a performance scientist and coach of Olympic athletes, team up to demystify these practices and demonstrate how everyone can achieve their best.The first book of its kind, Peak Performance combines the inspiring stories of top performers across a range of capabilities - from athletic, to intellectual, to artistic - with the latest scientific insights into the cognitive and neurochemical factors that drive performance in all domains. In doing so, Peak Performance uncovers new linkages that hold promise as performance enhancers but have been overlooked in our traditionally-siloed ways of thinking. The result is a life-changing book in which readers will learn how to enhance their performance by a myriad of ways including: optimally alternating between periods of intense work and rest; developing and harnessing the power of a self-transcending purpose; and priming the body and mind for enhanced productivity.In revealing the science of great performance and the stories of great performers across a wide range of capabilities, Peak Performance uncovers the secrets of success, and coaches readers on how to use them. If you want to take your game to the next level, whatever "your game" may be, Peak Performance will teach you how.