Informative yet so much fun to read!" Nassim Nicholas Taleb – author of The Black Swan "Easy-to-digest and highly competent. If everyone were to read this book, the world would become a more enlightened place." Gerd Gigerenzer – author of Gut Feelings: The Intelligence of the Unconsious Uneasy with how chance determines a huge part of our lives, we try to control what cannot be controlled and predict what cannot be predicted. It’s a self-delusion that can have serious consequences for our personal finances, careers, happiness and health. Dance with Chance explains how we all fall foul of this “Illusion of Control.” Fortunately, when we understand how luck operates, we can lessen its ill effects. By learning when to cede control over certain aspects of our life, paradoxically, we gain more control. By making small changes to our lifestyle, we can make a huge difference to the things that matter to us. From simple investment strategies to warning against health screenings, the authors offer revolutionary and often counterintuitive advice to make luck work for you. You’ll find out why millions of deaths are caused each year by medical negligence and why a billionaire is no happier than an Eskimo. You’ll discover why no-one predicted the worst financial crisis since the great depression and what makes a sports star. Witty, inventive, and informed by the latest findings in psychology and statistics, Dance with Chance is an essential guide to navigating the uncertain world in which we live. Spyros Makridakis is Distinguished Research Professor at INSEAD, Abu Dhabi Centre and a former Greek Olympian. Robin M. Hogarth is ICREA Research Professor at Universitat Pompeu Fabra, Spain. He was formerly a professor at the University of Chicago’s Graduate School of Business. Anil Gaba is the ORPAR Chaired Professor of Risk Management and Dean of Faculty at INSEAD, France and Singapore, the world’s second largest business school.

    Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

    Harvard Professor Lawrence Lessig warns that, if we're not careful we'll wake up one day to discover that the character of cyberspace has changed from under us. Cyberspace will no longer be a world of relative freedom; instead it will be a world of perfect control where our identities, actions, and desires are monitored, tracked, and analyzed for the latest market research report. Commercial forces will dictate the change, and architecture—the very structure of cyberspace itself—will dictate the form our interactions can and cannot take. Code And Other Laws of Cyberspace is an exciting examination of how the core values of cyberspace as we know it—intellectual property, free speech, and privacy-—are being threatened and what we can do to protect them. Lessig shows how code—the architecture and law of cyberspace—can make a domain, site, or network free or restrictive; how technological architectures influence people's behavior and the values they adopt; and how changes in code can have damaging consequences for individual freedoms. Code is not just for lawyers and policymakers; it is a must-read for everyone concerned with survival of democratic values in the Information Age.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

    The new edition, An Enduring Debate, continues the important discussion of nuclear proliferation and the dangers of a nuclear-armed world. With new chapters on the questions surrounding a nuclear North Korea, Iran, and Iraq and the potential for a world free of nuclear weapons, this Third Edition will continue to generate a lively classroom experience.

    The many aspects of our lives that are governed by social networks make it critical to understand how they impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do. In Social and Economic Networks, Matthew Jackson offers a comprehensive introduction to social and economic networks, drawing on the latest findings in economics, sociology, computer science, physics, and mathematics. He provides empirical background on networks and the regularities that they exhibit, and discusses random graph-based models and strategic models of network formation. He helps readers to understand behavior in networked societies, with a detailed analysis of learning and diffusion in networks, decision making by individuals who are influenced by their social neighbors, game theory and markets on networks, and a host of related subjects. Jackson also describes the varied statistical and modeling techniques used to analyze social networks. Each chapter includes exercises to aid students in their analysis of how networks function.This book is an indispensable resource for students and researchers in economics, mathematics, physics, sociology, and business.

    It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way.Data Visualization builds the reader's expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective "small multiple" plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible.Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings.Provides hands-on instruction using R and ggplot2Shows how the "tidyverse" of data analysis tools makes working with R easier and more consistentIncludes a library of data sets, code, and functions

    I have taught these simple processes to men and women all over the world, and recently over a thousand men and women of all religious affiliations attended a special class in Los Angeles where I presented the highlights of what is offered in the pages of this book. Many came from distances of two hundred miles for each class lesson. The special features of this book will appeal to you because they show you why oftentimes you get the opposite of what you prayed for and reveal to you the reasons why. People have asked me in all parts of the world and thousands of times, ?Why is it I have prayed and prayed and got no answer?? In this book you will find the reasons for this common complaint. The many ways of impressing the subconscious mind and getting the right answers make this an extraordinarily valuable book and an ever present help in time of trouble.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    For outsiders, Pyrrus usually means a quick and painful death, but DinAlt is fleeing the crooked casino masters of Cassylia - where he just broke the bank. But DinAlt is not prepared for the hellish Pyrrus, where every living thing seems bent on exterminating mankind.In DEATHWORLD 2 (originally published as THE ETHICAL ENGINEER), Jason DinAlt finds himself on a hostile, barbarian planet where technology and civilization have almost disappeared. Using his skills, DinAlt literally reinvents the wheel in his quest to escape and return to his friends on Pyrrus.

    

    It provides a concise and relevant treatment of inorganic chemistry and is written with such clarity that it is undoubtedly among the easiest to read of its competitors." Content "Atomic structure and the Periodic table Introduction to bonding The ionic bond The covalent bond The metallic bond General properties of the elements Coordination compounds Hydrogen and the hydrides Group 1 - The alkali metals The chlor-alkali industry Group 2 - The alkaline earth elements The group 13 elements The group 14 elements The group 15 elements Group 16 - the chalcogens Group 17 - the halogens Group 18 - the noble gases An introduction to the transition elements Group 3 - The scandium group Group 4 - The titanium group Group 5 - The vanadium group Group 6 - The chromium group Group 7 - The manganese group Group 8 - The iron group Group 9 - The cobalt group Group 10 - The nickel Group. Group 11 - The copper group: Coinage metals. Group 12 - The zinc group The lanthanide series. The actinides."

    Today, ML algorithms accomplish tasks that - until recently - only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.In the book, readers will learn how to:Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting.Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.