This book is significant because it is not only thoughtfully developed and based on careful reading of the extensive literature on leadership and governance, but it is also deliberately intended to enable the author to bridge the gap between theories of organization, on one hand, and practical application, on the other. --Journal of Higher Education

    It introduces the analysis of the key elements of language--sounds, words, structures and meanings, and provides a solid foundation in all of the essential topics. The third edition has been extensively revised to include new sections on important contemporary issues in language study, including language and culture, African American English, sign language, and slang. A comprehensive glossary provides useful explanations of technical terms, and each chapter contains a range of new study questions and research tasks, with suggested answers.

    A reference for health care clinicians and students, that takes account of the integration of alternative and complementary approaches into standard western medical care, defining terms relating to herbal remedies and traditional cures from other cultures.

    In case study format, this informative reference answers questions surrounding nuclear energy, the potential of sustainable fossil fuels, and the possibilities of sharing renewable power with foreign countries. While underlining the difficulty of minimizing consumption, the tone remains positive as it debunks misinformation and clearly explains the calculations of expenditure per person to encourage people to make individual changes that will benefit the world at large.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    It is a book for everybody who wants to learn how to write or who needs to do some writing to get through the day, as almost everybody does in the age of e-mail and the Internet. Whether you want to write about people or places, science and technology, business, sports, the arts or about yourself in the increasingly popular memoir genre, On Writing Well offers you fundamental priciples as well as the insights of a distinguished writer and teacher. With more than a million copies sold, this volume has stood the test of time and remains a valuable resource for writers and would-be writers.

    The text begins with basic skills related to personal experience and then carefully progresses to the more sophisticated reasoning skills required for abstract, academic contexts. Thinking Critically introduces students to the cognitive process while teaching them to develop their higher-order thinking and language abilities. A number of distinctive characteristics make the text an effective tool for both instructors and students. Exercises, discussion topics, and writing assignments encourage active participation, stimulating students to critically examine their own and others' thinking.

    So you want to write a novel? Great! That's a worthy goal, no matter what your reason. But don't settle for just writing a novel. Aim high. Write a novel that you intend to sell to a publisher. Writing Fiction for Dummies is a complete guide designed to coach you every step along the path from beginning writer to royalty-earning author. Here are some things you'll learn in "Writing Fiction for Dummies" * Strategic Planning: Pinpoint where you are on the roadmap to publication; discover what every reader desperately wants from a story; home in on a marketable category; choose from among the four most common creative styles; and learn the self-management methods of professional writers.* Writing Powerful Fiction: Construct a story world that rings true; create believable, unpredictable characters; build a strong plot with all six layers of complexity of a modern novel; and infuse it all with a strong theme.* Self-Editing Your Novel: Psychoanalyze your characters to bring them fully to life; edit your story structure from the top down; fix broken scenes; and polish your action and dialogue.* Finding An Agent and Getting Published: Write a query letter, a synopsis, and a proposal; pitch your work to agents and editors without fear.Writing Fiction For Dummies takes you from being a "writer" to being an "author." It can happen--if you have the talent and persistence to do what you need to do.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    We’re told that learning is all self-discipline, that we must confine ourselves to designated study areas, turn off the music, and maintain a strict ritual if we want to ace that test, memorize that presentation, or nail that piano recital.   But what if almost everything we were told about learning is wrong? And what if there was a way to achieve more with less effort?   In How We Learn, award-winning science reporter Benedict Carey sifts through decades of education research and landmark studies to uncover the truth about how our brains absorb and retain information. What he discovers is that, from the moment we are born, we are all learning quickly, efficiently, and automatically; but in our zeal to systematize the process we have ignored valuable, naturally enjoyable learning tools like forgetting, sleeping, and daydreaming. Is a dedicated desk in a quiet room really the best way to study? Can altering your routine improve your recall? Are there times when distraction is good? Is repetition necessary? Carey’s search for answers to these questions yields a wealth of strategies that make learning more a part of our everyday lives—and less of a chore.   By road testing many of the counterintuitive techniques described in this book, Carey shows how we can flex the neural muscles that make deep learning possible. Along the way he reveals why teachers should give final exams on the first day of class, why it’s wise to interleave subjects and concepts when learning any new skill, and when it’s smarter to stay up late prepping for that presentation than to rise early for one last cram session. And if this requires some suspension of disbelief, that’s because the research defies what we’ve been told, throughout our lives, about how best to learn.   The brain is not like a muscle, at least not in any straightforward sense. It is something else altogether, sensitive to mood, to timing, to circadian rhythms, as well as to location and environment. It doesn’t take orders well, to put it mildly. If the brain is a learning machine, then it is an eccentric one. In How We Learn, Benedict Carey shows us how to exploit its quirks to our advantage.  Praise for How We Learn“This book is a revelation. I feel as if I’ve owned a brain for fifty-four years and only now discovered the operating manual.”—Mary Roach, bestselling author of Stiff and Gulp“A welcome rejoinder to the faddish notion that learning is all about the hours put in.” —The New York Times Book Review   “A valuable, entertaining tool for educators, students and parents.” —Shelf Awareness   “How We Learn is more than a new approach to learning; it is a guide to making the most out of life. Who wouldn’t be interested in that?” —Scientific American   “I know of no other source that pulls together so much of what we know about the science of memory and couples it with practical, practicable advice.”—Daniel T. Willingham, professor of psychology at the University of Virginia

    It is the definitive reference guide, now in a second edition. Although the first edition was written in 1978, it continues to be a worldwide best-seller. This second edition brings the classic original up to date to include the ANSI standard. From the Preface: We have tried to retain the brevity of the first edition. C is not a big language, and it is not well served by a big book. We have improved the exposition of critical features, such as pointers, that are central to C programming. We have refined the original examples, and have added new examples in several chapters. For instance, the treatment of complicated declarations is augmented by programs that convert declarations into words and vice versa. As before, all examples have been tested directly from the text, which is in machine-readable form. As we said in the first preface to the first edition, C "wears well as one's experience with it grows." With a decade more experience, we still feel that way. We hope that this book will help you to learn C and use it well.

    Lesson plans and lecture notes matter less than the special way teachers comprehend the subject and value human learning. Whether historians or physicists, in El Paso or St. Paul, the best teachers know their subjects inside and out--but they also know how to engage and challenge students and to provoke impassioned responses. Most of all, they believe two things fervently: that teaching matters and that students can learn.In stories both humorous and touching, Bain describes examples of ingenuity and compassion, of students' discoveries of new ideas and the depth of their own potential. What the Best College Teachers Do is a treasure trove of insight and inspiration for first-year teachers and seasoned educators.

    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.

    New problems added throughout.

    This book is the most engaging, focused and applied HRM text on the market.