But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    Editorial pages applaud them for getting tough on crime. Constitutional lawyers raise the issue of cruel and unusual punishment. Legal philosophers ponder questions of justness. An economist, on the other hand, observes that making the punishment for armed robbery the same as that for murder encourages muggers to kill their victims. This is the cut-to-the-chase quality that makes economics not only applicable to the interpretation of law, but beneficial to its crafting.Drawing on numerous commonsense examples, in addition to his extensive knowledge of Chicago-school economics, David D. Friedman offers a spirited defense of the economic view of law. He clarifies the relationship between law and economics in clear prose that is friendly to students, lawyers, and lay readers without sacrificing the intellectual heft of the ideas presented. Friedman is the ideal spokesman for an approach to law that is controversial not because it overturns the conclusions of traditional legal scholars--it can be used to advocate a surprising variety of political positions, including both sides of such contentious issues as capital punishment--but rather because it alters the very nature of their arguments. For example, rather than viewing landlord-tenant law as a matter of favoring landlords over tenants or tenants over landlords, an economic analysis makes clear that a bad law injures both groups in the long run. And unlike traditional legal doctrines, economics offers a unified approach, one that applies the same fundamental ideas to understand and evaluate legal rules in contract, property, crime, tort, and every other category of law, whether in modern day America or other times and places--and systems of non-legal rules, such as social norms, as well.This book will undoubtedly raise the discourse on the increasingly important topic of the economics of law, giving both supporters and critics of the economic perspective a place to organize their ideas.

    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.

    Football's data revolution has only just begun. The arrival of advanced metrics and detailed analysis is already reshaping the modern game. We can now fully assess player performance, analyse the role of luck and measure what really leads to victory. There is no turning back.Now the race is on between football's wealthiest clubs and a group of outsiders, nerds and rule-breakers, who are turning the game on its head with their staggering innovations. Winning is no longer just about what happens out on the pitch, it's now a battle taking place in boardrooms and on screens across international borders with the world's brightest minds driving for an edge over their fiercest rivals.Christoph Biermann has moved in the midst of these disruptive upheavals, talking to scientists, coaches, managers, scouts and psychologists in the world's major clubs, traveling across Europe and the US and revealing the hidden - and often jaw-dropping - truths behind the beautiful game. 'A book full of exciting ideas and inside views on modern football. The most exciting book in an exciting time for football.' Thomas Hitzlsperger

    November 2015 is the 100th anniversary of Einstein’s discovery of the General Theory of Relativity.Levenson, head of MIT’s Science Writing Program, tells the captivating, unusual, and nearly-forgotten backstory behind Einstein’s invention of the Theory of Relativity, which completely changed the course of science forever. For over 50 years before Einstein developed his theory, the world’s top astronomers spent countless hours and energy searching for a planet, which came to be named Vulcan, that had to exist, it was thought, given Isaac Newton’s theories of gravity. Indeed, in the two centuries since Newton’s death, his theory had essentially become accepted as fact. It took Einstein’s genius to realize the mystery of the missing planet wasn’t a problem of measurements or math but of Newton’s theory of gravity itself. Einstein’s Theory of Relativity proved that Vulcan did not and could not exist, and that the decades-long search for it had merely been a quirk of operating under the wrong set of assumptions about the universe. Thomas Levenson tells this unique story, one of the strangest episodes in the history of science, with elegant simplicity, fast-paced drama, and lively characters sure to capture the attention of a wide group of readers.

    Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

    In Tammet's world, numbers are beautiful and mathematics illuminates our lives and minds. Using anecdotes, everyday examples, and ruminations on history, literature, and more, Tammet allows us to share his unique insights and delight in the way numbers, fractions, and equations underpin all our lives. Inspired by the complexity of snowflakes, Anne Boleyn's eleven fingers, or his many siblings, Tammet explores questions such as why time seems to speed up as we age, whether there is such a thing as an average person, and how we can make sense of those we love. Thinking In Numbers will change the way you think about math and fire your imagination to see the world with fresh eyes.