With deft use of examples of humor and wordplay, Steven Pinker weaves our vast knowledge of language into a compelling story: language is a human instinct, wired into our brains by evolution. The Language Instinct received the William James Book Prize from the American Psychological Association and the Public Interest Award from the Linguistics Society of America. This edition includes an update on advances in the science of language since The Language Instinct was first published.

    It is for teachers who refuse to harden their hearts, because they love learners, learning, and the teaching life." - Parker J. Palmer [from the Introduction] Teachers choose their vocation for reasons of the heart, because they care deeply about their students and about their subject. But the demands of teaching cause too many educators to lose heart. Is it possible to take heart in teaching once more so that we can continue to do what good teachers always do -- give heart to our students?In The Courage to Teach, Parker Palmer takes teachers on an inner journey toward reconnecting with their vocation and their students -- and recovering their passion for one of the most difficult and important of human endeavors.

    In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

    Now he brings his insight into the workplace, in a book sure to change the shape of business for decades to come.In Working with Emotional Intelligence, Goleman reveals the skills that distinguish star performers in every field, from entry-level jobs to top executive positions. He shows that the single most important factor is not IQ, advanced degrees, or technical expertise, but the quality Goleman calls emotional intelligence. Self-awareness, self-confidence, and self-control; commitment and integrity; the ability to communicate and influence, to initiate and accept change--these competencies are at a premium in today's job market. The higher up the leadership ladder you go, the more vital these skills become, often influencing who is hired or fired, passed over or promoted. As Goleman shows, we all possess the potential to improve our emotional intelligence--at any stage in our career. He provides guidelines for cultivating these capabilities--and also explains why corporate training must change if it is to be effective.

     Jammed with sage advice, genuine encouragement, and surprising examples of how to write and how not to write, this book gives beginning writers and confident students alike an easy-to-follow roadmap for improving one of the most important skills for success. En route to Sin Boldly!-induced, A+ paper bliss, readers encounter such topics as:Choosing a Topic and Telling Your Story ("K.I.S.S.-Keep It Simple, Stupid")Literary Games (featuring "Francobabble for Freshman")Choosing a Voice ("Dissing the Prof")Grammatical Horrors ("A does not equal they")Common Mistakes ("Hopefully and Other Controversies") Fully revised and updated with new examples, quizzes, and tips, Sin Boldly! is not only a comprehensive guide, but also a fantastic, fun read for anyone who wants to write clearly and effectively.

    Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

    After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layerperceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

    Burger teaches at Wiliams College; Starbird at The University of Texas at Austin. Here, they “reveal the hidden powers of deep understanding (earth), failure (fire), questions (air), the flow of ideas (water), and the quintessential element of change that brings all four elements together. By mastering and applying these practical and proven strategies, readers develop better thinking habits and learn how to create their own successes.”Brilliant people aren't a special breed--they just use their minds differently. By using the straightforward and thought-provoking techniques in "The 5 Elements of Effective Thinking," you will regularly find imaginative solutions to difficult challenges, and you will discover new ways of looking at your world and yourself--revealing previously hidden opportunities.The book offers real-life stories, explicit action items, and concrete methods that allow you to attain a deeper understanding of any issue, exploit the power of failure as a step toward success, develop a habit of creating probing questions, see the world of ideas as an ever-flowing stream of thought, and embrace the uplifting reality that we are all capable of change. No matter who you are, the practical mind-sets introduced in the book will empower you to realize any goal in a more creative, intelligent, and effective manner. Filled with engaging examples that unlock truths about thinking in every walk of life, "The 5 Elements of Effective Thinking" is written for all who want to reach their fullest potential--including students, parents, teachers, businesspeople, professionals, athletes, artists, leaders, and lifelong learners.Whenever you are stuck, need a new idea, or want to learn and grow, "The 5 Elements of Effective Thinking" will inspire and guide you on your way.

    This book helps students, academics and nonfiction writers to get more done, write intelligent texts and learn for the long run. It teaches you how to take smart notes and ensure they bring you and your projects forward. The Take Smart Notes principle is based on established psychological insight and draws from a tried and tested note-taking-technique. This is the first comprehensive guide and description of this system in English, and not only does it explain how it works, but also why. It suits students and academics in the social sciences and humanities, nonfiction writers and others who are in the business of reading, thinking and writing. Instead of wasting your time searching for notes, quotes or references, you can focus on what really counts: thinking, understanding and developing new ideas in writing. It does not matter if you prefer taking notes with pen and paper or on a computer, be it Windows, Mac or Linux. And you can start right away.

    If it takes a book to get it across, I hope this book will do it. It ought to.”—Thomas Schelling, Distinguished University Professor, School of Public Policy, University of Maryland, and 2005 Nobel Prize Laureate in Economics “With humor, insight, piercing logic and a nod to history, Ziliak and McCloskey show how economists—and other scientists—suffer from a mass delusion about statistical analysis. The quest for statistical significance that pervades science today is a deeply flawed substitute for thoughtful analysis. . . . Yet few participants in the scientific bureaucracy have been willing to admit what Ziliak and McCloskey make clear: the emperor has no clothes.”—Kenneth Rothman, Professor of Epidemiology, Boston University School of Health The Cult of Statistical Significance shows, field by field, how “statistical significance,” a technique that dominates many sciences, has been a huge mistake. The authors find that researchers in a broad spectrum of fields, from agronomy to zoology, employ “testing” that doesn’t test and “estimating” that doesn’t estimate. The facts will startle the outside reader: how could a group of brilliant scientists wander so far from scientific magnitudes? This study will encourage scientists who want to know how to get the statistical sciences back on track and fulfill their quantitative promise. The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots. Stephen T. Ziliak is the author or editor of many articles and two books. He currently lives in Chicago, where he is Professor of Economics at Roosevelt University. Deirdre N. McCloskey, Distinguished Professor of Economics, History, English, and Communication at the University of Illinois at Chicago, is the author of twenty books and three hundred scholarly articles. She has held Guggenheim and National Humanities Fellowships. She is best known for How to Be Human* Though an Economist (University of Michigan Press, 2000) and her most recent book, The Bourgeois Virtues: Ethics for an Age of Commerce (2006).

    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.

    Essentially, you make an initial guess, and then get more data to improve it. Bayes Theorem, or Bayes Rule, has a ton of real world applications, from estimating your risk of a heart attack to making recommendations on Netflix But It Isn't That Complicated This book is a short introduction to Bayes Theorem. It is only 15 pages long, and is intended to show you how Bayes Theorem works as quickly as possible. The examples are intentionally kept simple to focus solely on Bayes Theorem without requiring that the reader know complicated probability distributions. If you want to learn the basics of Bayes Theorem as quickly as possible, with some easy to duplicate examples, this is a good book for you.

    Today's environment is similarly chaotic, turbulent, and uncertain. On Becoming a Leader has served for nearly fifteen years as a beacon of insight, delving into the qualities that define leadership, the people who exemplify it, and the strategies that anyone can apply to become an effective leader. This new edition features a provocative introduction on the challenges and opportunities facing leaders today, with additional updates and current references throughout.

    James Paul Gee begins his new book with 'I want to talk about vide games- yes, even violent video games - and say some positive things about them'. With this simple but explosive beginning, one of America's most well-respected professors of education looks seriously at the good that can come from playing video games. Gee is interested in the cognitive development that can occur when someone is trying to escape a maze, find a hidden treasure and, even, blasting away an enemy with a high-powered rifle. Talking about his own video-gaming experience learning and using games as diverse as Lara Croft and Arcanum, Gee looks at major specific cognitive activities: How individuals develop a sense of identity; How one grasps meaning; How one evaluates and follows a command; How one picks a role model; How one perceives the world.

    Peter Bernstein has written a comprehensive history of man's efforts to understand risk and probability, beginning with early gamblers in ancient Greece, continuing through the 17th-century French mathematicians Pascal and Fermat and up to modern chaos theory. Along the way he demonstrates that understanding risk underlies everything from game theory to bridge-building to winemaking.