Feynman starts with an intuitive view of fundamental quantum mechanics, gradually introducing path integrals. Later chapters explore more advanced topics, including the perturbation method, quantum electrodynamics, and statistical mechanics. 1965 edition, emended in 2005.

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    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.

    He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the "prediction paradox": The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good-or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary-and dangerous-science.Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.

    The problem with this version of history? There’s not a shred of evidence to support it.Here anthropologist David Graeber presents a stunning reversal of conventional wisdom. He shows that for more than 5,000 years, since the beginnings of the first agrarian empires, humans have used elaborate credit systems to buy and sell goods—that is, long before the invention of coins or cash. It is in this era, Graeber argues, that we also first encounter a society divided into debtors and creditors. Graeber shows that arguments about debt and debt forgiveness have been at the center of political debates from Italy to China, as well as sparking innumerable insurrections. He also brilliantly demonstrates that the language of the ancient works of law and religion (words like “guilt,” “sin,” and “redemption”) derive in large part from ancient debates about debt, and shape even our most basic ideas of right and wrong. We are still fighting these battles today without knowing it.Debt: The First 5,000 Years is a fascinating chronicle of this little known history—as well as how it has defined human history, and what it means for the credit crisis of the present day and the future of our economy.

    But much of their work is based on assumptions that are untested generalizations at best, harmful misperceptions at worst.Abhijit Banerjee and Esther Duflo have pioneered the use of randomized control trials in development economics. Work based on these principles, supervised by the Poverty Action Lab, is being carried out in dozens of countries. Drawing on this and their 15 years of research from Chile to India, Kenya to Indonesia, they have identified wholly new aspects of the behavior of poor people, their needs, and the way that aid or financial investment can affect their lives. Their work defies certain presumptions: that microfinance is a cure-all, that schooling equals learning, that poverty at the level of 99 cents a day is just a more extreme version of the experience any of us have when our income falls uncomfortably low.This important book illuminates how the poor live, and offers all of us an opportunity to think of a world beyond poverty.Learn more at www.pooreconomics.com

    financial system, which had grown far too complex for its own good-and too unregulated for the public good-experienced a perfect storm beginning in 2007. When America's financial structure crumbled, the damage proved to be not only deep, but wide. It took the crisis for the world to discover, to its horror, just how truly interconnected-and fragile-the global financial system is. The second part of the story explains how American and international government intervention kept us from a total meltdown. Many of the U.S. government's actions, particularly the Fed's, were previously unimaginable. And to an amazing-and certainly misunderstood-extent, they worked. The worst did not happen. Blinder offers clear-eyed answers to the questions still before us, even if some of the choices ahead are as divisive as they are unavoidable. After the Music Stopped is an essential history that we cannot afford to forget, because one thing history teaches is that it will happen here again.

    Simmons advocates a careful approach to the subject, covering such topics as the wave equation, Gauss's hypergeometric function, the gamma function and the basic problems of the calculus of variations in an explanatory fashions - ensuring that students fully understand and appreciate the topics.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    This struggle has shaken the world economy, dictated the outcome of wars, and transformed the destiny of men and nations.The Prize is as much a history of the twentieth century as of the oil industry itself. The canvas of history is enormous -- from the drilling of the first well in Pennsylvania through two great world wars to the Iraqi invasion of Kuwait and Operation Desert Storm.

    These colorful and exciting techniques are hot on the lips of leading analysts and traders worldwide.

    Thaler has spent his career studying the radical notion that the central agents in the economy are humans—predictable, error-prone individuals. Misbehaving is his arresting, frequently hilarious account of the struggle to bring an academic discipline back down to earth—and change the way we think about economics, ourselves, and our world.Traditional economics assumes rational actors. Early in his research, Thaler realized these Spock-like automatons were nothing like real people. Whether buying a clock radio, selling basketball tickets, or applying for a mortgage, we all succumb to biases and make decisions that deviate from the standards of rationality assumed by economists. In other words, we misbehave. More importantly, our misbehavior has serious consequences. Dismissed at first by economists as an amusing sideshow, the study of human miscalculations and their effects on markets now drives efforts to make better decisions in our lives, our businesses, and our governments.Coupling recent discoveries in human psychology with a practical understanding of incentives and market behavior, Thaler enlightens readers about how to make smarter decisions in an increasingly mystifying world. He reveals how behavioral economic analysis opens up new ways to look at everything from household finance to assigning faculty offices in a new building, to TV game shows, the NFL draft, and businesses like Uber.Laced with antic stories of Thaler’s spirited battles with the bastions of traditional economic thinking, Misbehaving is a singular look into profound human foibles. When economics meets psychology, the implications for individuals, managers, and policy makers are both profound and entertaining.

    This is the view of Ayn Rand, a view so radically opposed to prevailing attitudes that it constitutes a major philosophic revolution. In this series of essays, she presents her stand on the persecution of big business, the causes of war, the default of conservatism, and the evils of altruism. Here is a challenging new look at modern society by one of the most provocative intellectuals on the American scene.   This edition includes two articles by Ayn Rand that did not appear in the hardcover edition: “The Wreckage of the Consensus,” which presents the Objectivists’ views on Vietnam and the draft; and “Requiem for Man,” an answer to the Papal encyclical Progresso Populorum.