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.

    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.

    These themes include mathematical reasoning, combinatorial analysis, discrete structures, algorithmic thinking, and enhanced problem-solving skills through modeling. Its intent is to demonstrate the relevance and practicality of discrete mathematics to all students. The Fifth Edition includes a more thorough and linear presentation of logic, proof types and proof writing, and mathematical reasoning. This enhanced coverage will provide students with a solid understanding of the material as it relates to their immediate field of study and other relevant subjects. The inclusion of applications and examples to key topics has been significantly addressed to add clarity to every subject. True to the Fourth Edition, the text-specific web site supplements the subject matter in meaningful ways, offering additional material for students and instructors. Discrete math is an active subject with new discoveries made every year. The continual growth and updates to the web site reflect the active nature of the topics being discussed. The book is appropriate for a one- or two-term introductory discrete mathematics course to be taken by students in a wide variety of majors, including computer science, mathematics, and engineering. College Algebra is the only explicit prerequisite.

    Over 100 additional new images are integrated into Volume I, and appear online as full size digital images with discussions written by the author. These bonus images are complemented by groundbreaking media support for students including video study tools and a robust eBook.

    

    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.

    Politicians claim they care, then pass legislation that just sends more cash to the HMOs. Wages have been stagnant for thirty years, even as corporate profits skyrocket. Politicians say they want to fix the problem and then pass bills written by lobbyists that drive wages even lower and punish those crushed by debt. Jobs are being shipped overseas, pensions are being cut, and energy is becoming unaffordable. And our government, more concerned about maintaining its corporate sponsorship than protecting its citizens, does nothing about it. In Hostile Takeover, David Sirota, a major new voice in American politics, seeks to open the eyes of ordinary Americans to the fact that corporate interests have undermined democracy, aided and abetted by their lackeys in our allegedly representative government. At a time when more and more of America’s major political leaders are being indicted or investigated for corruption, Sirota takes readers on a journey that shows how all of this nefarious behavior happened right under our noses—and how the high-profile scandals are merely one product of a political system and debate wholly owned by Big Money interests. Sirota considers major public issues that feel intractable—like spiraling health care costs, the outsourcing of jobs, the inequities of the tax code, and out-of-control energy prices—and shows how in each case workable solutions are buried under the lies of lobbyists, the influence of campaign cash, and the ubiquitous spin machine financed by Big Business.With fiery passion, pinpoint wit, and lucid analysis, Hostile Takeover reveals the true enemies of reform and their increasingly sophisticated—and hostile—tactics. It’s an essential guidebook for those of us tired of the government selling us out—and determined to take our country back. Also available as an eBookFrom the Hardcover edition.

    Why?In an era of growing cultural pessimism, many thoughtful individuals believe that different civilisations – especially Islam and the West – cannot live together in peace. The ten countries of ASEAN provide a thriving counter-example of civilizational co-existence. Here 625m people live together in peace. This miracle was delivered by ASEAN.In an era of growing economic pessimism, where many young people believe that their lives will get worse in coming decades, Southeast Asia bubbles with optimism. In an era where many thinkers predict rising geopolitical competition and tension, ASEAN regularly brings together all the world’s great powers.Stories of peace are told less frequently than stories of conflict and war. ASEAN’s imperfections make better headlines than its achievements. But in the hands of thinker and writer Kishore Mahbubani, the good news story is also a provocation and a challenge to the rest of the world."This excellent book explains, in clear and simple terms, how and why ASEAN has become one of the most successful regional organizations in the world."George Yeo"A powerful and passionate account of how, against all odds, ASEAN transformed the region and why Asia and the world need it even more today."Amitav Acharya“Kishore and I have written that the world is coming together in a Fusion of Civilisations. This book documents beautifully how ASEAN has achieved this fusion. The ASEAN story is hugely instructive and this book tells it very well.”Larry SummersKishore Mahbubani is Dean of the Lee Kuan Yew School of Public Policy, National University of Singapore, and author of The New Asian Hemisphere: The Irresistible Shift of Global Power to the East. Jeffery Sng is a writer and former diplomat based in Bangkok, co-author of A History of the Thai-Chinese.

    No statistical knowledge is required, and your programming skills can range from hobbyist to pro.Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: Create artful graphs to visualize complex data sets and functions Write more efficient code using parallel R and vectorization Interface R with C/C++ and Python for increased speed or functionality Find new R packages for text analysis, image manipulation, and more Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

    It givessyntax and output for accomplishing many analyses through the mostrecent releases of SAS, SPSS, and SYSTAT, some not available insoftware manuals. The book maintains its practical approach, stillfocusing on the benefits and limitations of applications of a techniqueto a data set -- when, why, and how to do it. Overall, it providesadvanced students with a timely and comprehensive introduction totoday's most commonly encountered statistical and multivariatetechniques, while assuming only a limited knowledge of higher-levelmathematics.

    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

    It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

    This new edition is written in a deliberate manner that has students following the program planning process in a logical manner. Students will learn to track one phase to the next, resulting in a solid understanding of the issues of internal consistency and planning integrity. The book′s format guides students from problem analysis through evaluation, enabling students to apply these concepts to their own program plans.

    This friendly, easy-to-read guide gently introduces you to the most commonly used features of SAS software plus a whole lot more! Authors Lora Delwiche and Susan Slaughter have revised the text to include concepts of the Output Delivery System; the STYLE= option in the PRINT, REPORT, and TABULATE procedures; ODS HTML, RTF, PRINTER, and OUTPUT destinations; PROC REPORT; more on PROC TABULATE; exporting data; and the colon modifier for informats. You'll find clear and concise explanations of basic SAS concepts (such as DATA and PROC steps), inputting data, modifying and combining data sets, summarizing and presenting data, basic statistical procedures, and debugging SAS programs. Each topic is presented in a self-contained, two-page layout complete with examples and graphics. This format enables new users to get up and running quickly, while the examples allow you to type in the program and see it work!

    The result is described as “a thought-provoking visual feast”.