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.

    This text shows how interpersonal communication theory and skills pertain to students' daily encounters with others.

    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.

    Offering time-tested problems, conceptual and visual pedagogy, and a state-of-the-art media package, this 11th edition looks to the future of university physics, in terms of both content and approach.

    The main objective of this book is to present circuit analysis in a clear, easy-to-understand manner, with many practical applications to interest the student. Each chapter opens with either historical sketches or career information on a subdiscipline of electrical engineering. This is followed by an introduction that includes chapter objectives. Each chapter closes with a summary of the key points and formulas. The authors present principles in an appealing and lucid step-by-step manner, carefully explaining each step. Important formulas are highlighted to help students sort out what is essential and what is not. Many pedagogical aids reinforce the concepts learned in the text so that students get comfortable with the various methods of analysis presented in the text.

     They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.

    /B.TECH., U.P.S.E.(ENGG..SERVICES ) : SECTION 'B' OF A.M.I.E. (I) Table of Contents Introdeuction Kinematics Of Motion Kinetics Of Motion Simple Harmonic Motion Simple Mechanisms Velocity In Mechanims ( Instantaneous Centre Method) Velocity In Mechanims ( Relative Centre Method ) Acceleration In Mechanisms Mechanisms With Lower Pairs Friction Belt, Rope & Chain Drives Toothed Gearing Gear Trains Gyroscopic Couple &Precessional Motion Inertia Forces In Reciprocating Parts Turning Moment Diagrams & Flywheel Steam Engine Valves & Reversing Gears Governors Brakes & Dynamometers Cams Balancing Of Rotating Masses Balancing Of Reciprocating Masses Longitudinal & Transverse Vibrations Torsional Vibrations Computer Aided Analysis & Synthesis Of Mechanisms Automatic Control

    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.

    John Seely Brown and Paul Duguid argue that the gap between digerati hype and end-user gloom is largely due to the "tunnel vision" that information-driven technologies breed. We've become so focused on where we think we ought to be--a place where technology empowers individuals and obliterates social organizations--that we often fail to see where we're really going.The Social Life of Information shows us how to look beyond our obsession with information and individuals to include the critical social networks of which these are always a part.

    That's the bad news. Here's the great news: Social Media and Web 2.0 offer you an unprecedented opportunity to make PR work better than ever before. This book shows how to reinvent PR around two-way conversations, bring the "public" back into public relations and get results that traditional PR people can only dream about. Drawing on their unparalleled experience making Social Media work for business, PR 2.0.com's Brian Solis and industry leader Deirdre Breakenridge show how to transform the way you think, plan, prioritize, and deliver PR services. You'll learn powerful new ways to build the relationships that matter, and reach a new generation of influencers...leverage platforms ranging from Twitter to Facebook...truly embed yourself in the communities that are shaping the future. Best of all, you won't just learn how to add value in the Web 2.0 world: You'll learn how to prove how new, intelligent, and socially rooted PR will transform your organization into a proactive, participatory communication powerhouse that is in touch and informed with its community of stakeholders.

    Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he'd seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support.

    Students are provided with an exceptionally clear and coherent understanding of child development, emphasizing the interrelatedness of all domains physical, cognitive, emotional, and social throughout the text narrative and in special features. Focusing on education and social policy as critical pieces of the dynamic system in which the child develops, Berk pays meticulous attention to the most recent scholarship in the field. Berk helps students connect their learning to their personal and professional areas of interest and their future pursuits as parents, educators, heath care providers, counselors, social workers, and researchers."

    A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.

    It is a ‘ must’ book for those in every branch of science . . . in addition, economists, politicians, statesmen, and businessmen cannot afford to overlook cybernetics and its tremendous, even terrifying implications. "It is a beautifully written book, lucid, direct, and despite its complexity, as readable by the layman as the trained scientist." -- John B. Thurston, "The Saturday Review of Literature" Acclaimed one of the "seminal books . . . comparable in ultimate importance to . . . Galileo or Malthus or Rousseau or Mill," "Cybernetics" was judged by twenty-seven historians, economists, educators, and philosophers to be one of those books published during the "past four decades", which may have a substantial impact on public thought and action in the years ahead." -- Saturday Review

    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.