Stanford Wong has contributed to Professional Blackjack .Wong is one of the country's leading gambling authorities.

    

    This second edition is appropriate as a supplementary (companion) text for any college-level course covering digital signal processing.

    Now that its basketball program has fallen apart, CSU’s only claim to fame is its Gravinics Department, dedicated to the study of an obscure European country—its mythology, its extraordinarily difficult language, and especially its bizarre star poet, Henderson.Having discovered Henderson’s poetry in a trash bin, Stanley Higgs becomes the foremost scholar of the poet’s work, accepts a position at Chandler State University, achieves international academic fame, marries the Dean’s daughter, and abruptly stops talking. With all of academia convinced that Higgs is formulating a great truth, the university employs Orwellian techniques to record Higgs’s every potential utterance and to save its reputation. A feckless Gravinics language student, Samuel Grapearbor, together with his long-suffering girlfriend Julia, is hired to monitor Higgs during the day. Over endless games of checkers and shared sandwiches, a uniquely silent friendship develops. As one man struggles to grow up and the other grows old, The Grasshopper King, in all of his glory, emerges.In this debut novel about treachery, death, academia, marriage, mythology, history, and truly horrible poetry, Jordan Ellenberg creates a world complete with its own geography, obscene folklore, and absurdly endearing -characters—a world where arcane subjects flourish and the smallest swerve from convention can result in -immortality.Jordan Ellenberg was born in Potomac, Maryland in 1971. His brilliance as a mathematical prodigy led to a feature in The National Enquirer, an interview with Charlie Rose on CBS’s Nightwatch, and gold medals at the Math Olympiad in Cuba and Germany. He is now an Assistant Professor of Math at Princeton University and his column, "Do the Math," appears regularly in the online journal Slate. This is his first novel.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    This text aims to motivate the need for tools with concrete applications, providing simple assumptions that match the application.

    Changes in the fifth edition include: A new chapter on credit derivatives (Chapter 21). New! Business Snapshots highlight real-world situations and relevant issues. The first six chapters have been -reorganized to better meet the needs of students and .instructors. A new release of the Excel-based software, DerivaGem, is included with each text. A useful Solutions Manual/Study Guide, which includes the worked-out answers to the "Questions and Problems" sections of each chapter, can be purchased separately (ISBN: 0-13-144570-7).

    

    In this book, Didier Sornette boldly applies his varied experience in these areas to propose a simple, powerful, and general theory of how, why, and when stock markets crash.Most attempts to explain market failures seek to pinpoint triggering mechanisms that occur hours, days, or weeks before the collapse. Sornette proposes a radically different view: the underlying cause can be sought months and even years before the abrupt, catastrophic event in the build-up of cooperative speculation, which often translates into an accelerating rise of the market price, otherwise known as a "bubble." Anchoring his sophisticated, step-by-step analysis in leading-edge physical and statistical modeling techniques, he unearths remarkable insights and some predictions--among them, that the "end of the growth era" will occur around 2050.Sornette probes major historical precedents, from the decades-long "tulip mania" in the Netherlands that wilted suddenly in 1637 to the South Sea Bubble that ended with the first huge market crash in England in 1720, to the Great Crash of October 1929 and Black Monday in 1987, to cite just a few. He concludes that most explanations other than cooperative self-organization fail to account for the subtle bubbles by which the markets lay the groundwork for catastrophe.Any investor or investment professional who seeks a genuine understanding of looming financial disasters should read this book. Physicists, geologists, biologists, economists, and others will welcome "Why Stock Markets Crash" as a highly original "scientific tale," as Sornette aptly puts it, of the exciting and sometimes fearsome--but no longer quite so unfathomable--world of stock markets.

    Perhaps you've heard that Walt Disney kept an apartment on Main Street? Or that there is a secret, members-only club in New Orleans Square? It's all true... but these are only two of the interesting facts pointed out in "101 Things You Never Knew About Disneyland." Many of the stories here deal with more obscure, yet equally interesting, histories of Disneyland. In fact, the majority of the material comes straight from interviews with the Imagineers who helped to build, maintain, and revitalize Disneyland. The book pays special attention to the inside jokes and hidden references to Disneyland's past, as these samples illustrate: Do you know which play on words pays tribute to a former Mexican restaurant at a certain site? Do you know how Tarzan's Treehouse honors its predecessor in two different ways? Do you know what is so unusual about the decorations atop "It's a Small World"? Do you know why one palm tree in Adventureland is special? Do you know what the sign says at the bottom of Splash Mountain's big drop? Do you know which pumpkin is an homage to a Disneyland official? It's a certainty that you haven't heard all these stories before. Although there are 101 such highlighted stories, each comes with a fuller description and is followed by a second story that is thematically related, so this volume really offers 202 Things you haven't heard about Disneyland before. You will find these facts delightful and unforgettable once you've heard them!

    Offering inspiration and intellectual delight, the problems throughout the book encourage students to express their ideas in writing to explain how they conceive problems, what conjectures they make, and what conclusions they reach. Applying specific techniques and strategies, readers will acquire a solid understanding of the fundamental concepts and ideas of number theory.

    This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.-- "Journal of Economics"

    GRAPH THEORY WITH APPLICATIONS TO ENGINEERING AND COMPUTER SCIENCE-PHI-DEO, NARSINGH-1979-EDN-1

    

    Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.