-Titulo Original : The Machinery Of Life-Fabricante : Springe...

-Titulo Original : Cracked It! How To Solve Big Problems And Sell Solutions Like Top Strategy Consultants-Fabricante : Springer-Descripcion Original: Solving complex problems and selling their solutions is critical for personal and organizational success. For most of us, however, it doesnt come naturally and we havent been taught how to do it well. Research shows a host of pitfalls trips us up when we try: Were quick to believe we understand a situation and jump to a flawed solution. We seek to confirm our hypotheses and ignore conflicting evidence. We view challenges incompletely through the frameworks we know instead of with a fresh pair of eyes. And when we communicate our recommendations, we forget our reasoning isnt obvious to our audience. How can we do it better? In Cracked It!, seasoned strategy professors and consultants Bernard Garrette, Corey Phelps and Olivier Sibony present a rigorous and practical four-step approach to overcome these pitfalls. Building on tried-and-tested (but rarely revealed) methods of top strategy consultants, research in cognitive psychology, and the latest advances in design thinking, they provide a step-by-step process and toolkit that will help readers tackle any challenging business problem. Using compelling stories and detailed case examples, the authors guide readers through each step in the process: from how to state, structure and then solve problems to how to sell the solutions. Written in an engaging style by a trio of experts with decades of experience researching, teaching and consulting on complex business problems, this book will be an indispensable manual for anyone interested in creating value by helping their organizations crack the problems that matter most. Review If you want to master problem solving, buy this book. I used it the day after I started reading it. - Neil Janin, Chairman Bank of Georgia and Senior Partner Emeritus, McKinsey & Co.Too many companies fail because of bad strategic problem solving. This book is full of frameworks and tips to help you avoid that fate. - Adam Grant, New York Times bestselling author of Give and Take, Originals, and Option B with Sheryl SandbergStrategy is problem solving - an important, subtle and pervasive skill for which business practitioners receive little formal training. Cracked It! provides a valuable and practical guide to the art, from framing the problem through to communicating and selling the solution, which should be invaluable to practitioners and consultants - Martin Reeves, Senior Partner & Managing Director, Boston Consulting GroupWritten by a trio of leading experts on strategy who draw on decades of rigorous academic research and consulting experience, Cracked It! offers a crystal-clear approach to frame and solve challenging strategic issues. A must-read for executives, consultants, and business school students. - Laurence Capron, co-author of Build, Borrow or Buy and Professor of Strategy and Dean of Faculty, INSEADWhen dealing with a major business issue, every decision maker faces his own cognitive biases that can trip him up. Cracked it! provides practical, value-creating and proven insight to develop and sell sound solutions by overcoming these biases. - Stephane Richard, Chairman and CEO, OrangeThe ABSOLUTE reference handbook on problem solving! It is clearly unique and it smartly introduces an amazing richness of methods, through cases and easy-to-understand frameworks. I have to say... I love it! - Jean-Baptiste Voisin, Chief Strategy Officer, LVMH From the Back Cover Solving complex problems and selling their solutions is critical for personal and organizational success. For most of us, however, it doesn’t come naturally and we haven’t been taught how to do it well. Research shows a host of pitfalls trips us up when we try: We’re quick to believe we understand a situation and jump to a flawed solution. We seek to confirm our hypotheses and ignore conflicting evidence. We view challenges incompletely through the frameworks we know instead of with a fresh pair of eyes. And when we communica...

-Titulo Original : Confessions Of The Pricing Man How Price Affects Everything-Fabricante : Springer-Descripcion Original: From the Back Cover The world’s foremost expert on pricing strategy shows how this mysterious process works and how to maximize value through pricing to company and customer. In this engaging and practical narrative, Hermann Simon leaves nothing out of the pricing cocktail, explaining each ingredient through dozens of stories collected over four decades in the trenches and behind the scenes. A world-renowned speaker on pricing and a trusted advisor to Fortune 500 executives, Simon’s lifelong journey has taken him from rural farmers’ markets, to a distinguished academic career, to a long second career as an entrepreneur and management consultant to companies large and small throughout the world. Along the way, he has learned from Nobel Prize winners and leading management gurus, and helped countless managers and executives use pricing as a way to create new markets, grow their businesses, and gain a sustained competitive advantage. Here, for consumers and business people alike, he reveals the secrets of successful pricing practices, and shares the wisdom and common sense that will forever change the way you thinking about pricing. “This is the best book I know to open your mind to the practical problems in setting your prices.”PHILIP KOTLER, S.C. Johnson & Son Distinguished Professor of International Marketing at the Kellogg School of Management, Northwestern University. “Hermann Simon is ‘The Pricing Man’ and a giant within our discipline. With more than 40 years of experience in pricing research and practice with companies across the globe and across all major industries, he possesses the know-how to help organizations large, medium-sized, and small to improve their pricing acumen. This book will help you improve your pricing management skills, strategies, and tactics - and it will help your company also.” KEVIN MITCHELL, President, The Professional Pricing Society, Inc. “Hermann Simon is a man who can get upset about being offered a 35 percent discount on a new digital camera. His fascination with ‘willingness to pay’ is infectious. As much as any individual, he is behind the professionalization of pricing in the past couple of decades.”WILLIAM POUNDSTONE, Author, Priceless: The Myth of Fair Value (and How to Take Advantage of It) The world’s foremost expert on pricing strategy shows how this mysterious process works and how to maximize value through pricing to company and customer.In all walks of life, we constantly make decisions about whether something is worth our money or our time, or try to convince others to part with their money or their time. Price is the place where value and money meet. From the global release of the latest electronic gadget to the bewildering gyrations of oil futures to markdowns at the bargain store, price is the most powerful and pervasive economic force in our day-to-day lives and one of the least understood.The recipe for successful pricing often sounds like an exotic cocktail, with equal parts psychology, economics, strategy, tools and incentives stirred up together, usually with just enough math to sour the taste. That leads managers to water down the drink with hunches and rules of thumb, or leave out the parts with which they don’t feel comfortable. While this makes for a sweeter drink, it often lacks the punch to have an impact on the customer or on the business.It doesn’t have to be that way, though, as Hermann Simon illustrates through dozens of stories collected over four decades in the trenches and behind the scenes. A world-renowned speaker on pricing and a trusted advisor to Fortune 500 executives, Simon’s lifelong journey has taken him from rural farmers’ markets, to a distinguished academic career, to a long second career as an entrepreneur and management consultant to companies large and small throughout the world. Along the way, he has learned from Nobel Prize winners and leading management gurus, and helped countless managers and executi...

-Titulo Original : The Ceo’s Secret Weapon How Great Leaders And Their Assistants Maximize Productivity And Effectiveness-Fabricante : Springer-Descripcion Original: Review P { margin-bottom: 0.08in; }A:link { } What makes a leader great? An executive assistant who makes sure he or she shows up at the right place at the right time well prepared. EAs are the wizards behind the curtain that make business magic happen. If you want to become a better leader, or a better EA, you will treasure this book. - Chester Elton, New York Times best-selling author of All In, What Motivates Me and The Carrot Principle.Two world business icons - Richard Branson and Donald Trump - have shared with Jan Jones how they stay ahead of the game. In her book, Jan (a former top EA herself) provides an insiders look into how executives can give themselves the advantage of a Branson or a Trump, through a powerful working partnership with a top-level assistant. - Khaled Baydoun, Founder & CEO, Bookurstar Middle East & North Africa RegionExecutives and assistants alike will benefit from the wisdom she shares in this book. - Rick Olafsson, Co-Founder, Appy Entertainment, Inc.Executive assistants need to be positive, tenacious, resourceful, reliable and gracious. Luckily for executives, Jan Jones shows you how to get the best out of the most important asset you have in the office, your own assistant. - Dana Kyle, former assistant to Dr. Ken Blanchard; Knowledge Sharing Administrator, The Ken Blanchard CompaniesWith this book, assistants worldwide will have access to Jans expertise on how they can develop powerful partnerships with their executives, while increasing their own productivity and effectiveness. - Karen Beattie, Managing Director, The Growth Faculty, AustraliaJan Jones has finally written the book so many executives and assistants have repeatedly asked her to write, about how executives and their assistants can work together effectively. This book will be invaluable to executives and assistants who want to maximize their productivity and develop their partnership. - Suzanne M. Kelly, MSOL; Editor, Urantia International Journal; Secretary, Urantia International Association, Texas BranchWhether you are an executive or an assistant, you will gain much from reading Jans book. - Andrea Temel, Speaker Manager, Speakers For Success, Irvine, California.Jan holds herself to the highest standards. Shes written this book so others can benefit from her actionable strategies, ideas and tips. I am one of Jans biggest fans. Read her book now so that the results she creates for her clients will be yours. - Michael Hutchison, former VP of Marketing and Sales for Tony Robbins; Host, Dunn & Bradstreet TV show CredibilityLive.As Jan Jones assistant at the Tony Robbins organization, I was the direct beneficiary of the invaluable advice she shares in this book. She showed me what being resourceful and remaining calm under extreme pressure truly meant. Executives and assistants who want to maximize their productivity should read this book carefully and follow its advice. -Veronique Franceus, former assistant to Jan Jones and Tony Robbins; Associate Learning Specialist, Bristol-Myers Squibb Many executives dont take full advantage of the assistant who sits right outside their door. This book educates executives about all the ways in which they can streamline and improve the way they work with the help of a great assistant, while teaching them to identify great candidates and maximize the benefits of this special relationship. About the Author Jan Jones is founder and president of Jan Jones Worldwide, a bespoke speakers bureau which evolved from her industry experience as executive assistant to personal development icon, Anthony Robbins, and ten years as exclusive representative for small business guru and entrepreneurial visionary, Michael Gerber. Prior to starting her business, Jan spent almost twenty years as executive assistant to successful businesspeople around the world, including Australian computer industry pioneer, Lionel Singer. Jan remains a passiona...

-Titulo Original : The 100 Best Astrophotography Targets A Monthly Guide For Ccd Imaging With Amateur Telescopes (the Patrick Moore Practical Astronomy Series)-Fabricante : Springer-Descripcion Original: Any amateur astronomer who is interested in astrophotography, particularly if just getting started, needs to know what objects are best for imaging in each month of the year. These are not necessarily the same objects that are the most spectacular or intriguing visually. The camera reveals different things and has different requirements. What objects in the sky tonight are large enough, bright enough, and high enough to be photographed? This book reveals, for each month of the year, the choicest celestial treasures within the reach of a commercial CCD camera. Helpful hints and advice on framing, exposures, and filters are included. Each deep sky object is explained in beautiful detail, so that observers will gain a richer understanding of these astronomical objects. This is not a book that dwells on the technology of CCD, Webcam, wet, or other types of astrophotography. Neither is it a book about in-depth computer processing of the images (although this topic is included). Detailed discussions of these topics can be found in other publications. This book focuses on what northern latitude objects to image at any given time of the year to get the most spectacular results. Review From the reviews: “Kier comes to the rescue with targets that are well placed at different times during each month. … Each target has a colour image and a description, plus advice on the equipment you’ll need. There’s useful advice on processing too. … seasoned imagers will enjoy this book.” (Steve Richards, Sky at Night Magazine, February, 2010) “This book is one of the Patrick Moore’s Practical Astronomy series from Springer, aimed at the practising … amateur astronomer. … The book’s intent is to provide a visually compelling list of the 100 most interesting deep sky objects from the perspective of a digital imager, more precisely of the CCD user. … This book is a good starter for going beyond the early shots and provides a reasonable reference on what to expect from each target … .” (Andrea Tasselli, Journal of the British Astronomical Association, Vol. 120 (1), 2010) “Ruben Kier, a highly accomplished astrophotographer, has compiled an impressive list of 100 objects, arranged in order of when they are best placed for observers in the Northern Hemisphere. … Though the book is clearly aimed at the established imager … . I was particularly impressed at how well some deep sky objects can now be imaged even with the full moon in the sky. The book will serve as an inspiration to those wishing to take their CCD imaging to the next level.” (Neil English, Astronomy Now, May, 2010) From the Back Cover Astronomical observing and photography are favorite pastimes of yours. You want to combine the two, but you’re not sure how. Or perhaps you have dabbled in astronomy for a while and want to take another step. What do you photograph? Will something that looks amazing as you peer at it through a telescope look the same in a photograph? There are so many dazzling sights in the night sky. How to choose? Ruben Kier has some answers for you. With his technical expertise and wide experience as both a visual observer and a photographer, he can help you attain some of the best images you can imagine, perhaps ones you will want to send to a magazine or proudly put up on your website. And the secret is - it’s not that hard! It’s mostly a matter of choosing the right subjects and then doing the necessary post-processing to get results that will dazzle. So get out there on the next clear night and create something to show for your efforts. Your friends will be impressed, and you will be thrilled at how you are able to combine the two passions of your life into one...

-Titulo Original : All Of Nonparametric Statistics (springer Texts In Statistics)-Fabricante : Springer-Descripcion Original: This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory. Review From the reviews: ...The book is excellent. (Short Book Reviews of the ISI, June 2006) Now we have All of Nonparametric Statistics … the writing is excellent and the author is to be congratulated on the clarity achieved. … the book is excellent. (N.R. Draper, Short Book Reviews, 26:1, 2006) Overall, I enjoyed reading this book very much. I like Wassermans intuitive explanations and careful insights into why one path or approach is taken over another. Most of all, I am impressed with the wealth of information on the subject of asymptotic nonparametric inferences. (Stergios B. Fotopoulos for Technometrics, 49:1, February 2007) The intention of this book is to give a single source with brief accounts of modern topics in nonparametric inference. … The text is a mixture of theory and applications, and there are lots of examples … . The text is also illustrated with many informative figures. … this book covers many topics of modern nonparametric methods, with focus on estimation and on the construction of confidence sets. It should be a useful reference for anyone interested in the theories and methods of this area. (Andreas Karlsson, Statistical Papers, 48, 2006) ...ANPS provides an excellent complement or a complete course textbook with a mixture of theoretical and computational exercises. ...For a book in a rapidly evolving field, the content and references are quit eup to date. ...As advertised, it offers a well-written, albeit brief account of numerous topics in modern nonparametric inference. (Greg Ridgeway, Journal of the American Statistical Association, Vol. 102, No. 477, 2007) This is a nicely written textbook oriented mainly to master level statistics and computer science students. The author provides wide a coverage of modern nonparametric methods … . the key ideas and basic proofs are carefully explained. Bibliographic remarks point the reader to references that contain further details. Each chapter is finished with useful exercises … . The book is also suitable for researchers in statistics, machine learning, and data mining. (Oleksandr Kukush, Zentralblatt MATH, Vol. 1099 (1), 2007) From the Back Cover The goal of this text is to provide the reader with a single book where they can find a brief account of many, modern topics in nonparametric inference. The book is aimed at Masters level or Ph.D. level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. This text covers a wide range of topics including: the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book has a mixture of methods and theory. Larry Wasserman is Professor of Statistics at Carnegie Mellon University and a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, multiple testing, and applications to astrophysics, bioinformatics and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents Award and the 2002 winner of the Centre de recherches mathematique...

-Titulo Original : Pattern Recognition And Machine Learning (information Science And Statistics)-Fabricante : Springer-Descripcion Original: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. Review From the reviews: This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group. John Maindonald for the Journal of Statistical Software In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields. (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007) This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study. (W. R. Howard, Kybernetes, Vol. 36 (2), 2007) Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals. (C. Tappert, CHOICE, Vol. 44 (9), May, 2007) The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … . (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007) This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value. (H. G. Feichtinger, Monatshefte fur Mathematik, Vol. 151 (3), 2007) Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recogni...

-Titulo Original : A Primer Of Ecology With R (use R!)-Fabricante : Springer-Descripcion Original: Provides simple explanations of the important concepts in population and community ecology. Provides R code throughout, to illustrate model development and analysis, as well as appendix introducing the R language. Interweaves ecological content and code so that either stands alone. Supplemental web site for additional code. Review From the reviews: “…I found the text to be an excellent foray into ecological models using R. I plan to use this book in my upcoming modeling course for upper-level undergraduates, a course cross-listed in the math and biology departments. The models will present some challenges for my students but I think the pace of the text will work for them. In addition, many of my students will be new to R, and to programming, but the text does a great job of integrating an introduction to R with the models. I can see this book being valuable to graduate students and research ecologists wishing to work with these foundational models in R. It is now time to jump into R!” (Ecology, 91(4), 2010) “Ecology is a complex discipline that can best be understood by making suitable abstractions, or models. The simplest mathematical models are composed of general rules and rarely require more than two equations. These models have the advantage that they apply to a variety of systems. The book…primarily focuses on population dynamics, a field where such simple models are commonly used. …the novelty lies in the tools that are used to make the theory work and that will undoubtedly bring this type of analysis closer to the individual researcher and the students. …Undoubtedly, this book will contribute to the further democratization of mathematical modeling and the use of R in this field. Given the variety of the topics covered, the highly readable text, and the ready-to-use code excerpts, I consider the book an absolute must for those who are active or intend to work in the field of population modeling.” (Journal of Statistical Software, January 2010, Vol. 32, Book Review 3) “This volume fills an important niche in the ecology textbook community. Like other primers, this book covers many of the core concepts of ecology, particularly population ecology, but Stevens goes into greater depth, presenting many of the complexities of core ecological processes. … an excellent volume for graduate-level courses in ecology and will be useful to ecologists who desire to refamiliarize themselves with core ecological concepts while learning computational analysis and modeling techniques.” (Matthew Aiello-Lammens, The Quarterly Review of Biology, Vol. 85 (3), 2010) From the Back Cover Ecology is more quantitative and theory-driven than ever before, and A Primer of Ecology with R combines an introduction to the major theoretical concepts in general ecology with a cutting edge open source tool, the R programming language. Starting with geometric growth and proceeding through stability of multispecies interactions and species-abundance distributions, this book demystifies and explains fundamental ideas in population and community ecology. Graduate students in ecology, along with upper division undergraduates and faculty, will find this to be a useful overview of important topics. In addition to the most basic topics, this book includes construction and analysis of demographic matrix models, metapopulation and source-sink models, host-parasitoid and disease models, multiple basins of attraction, the storage effect, neutral theory, and diversity partitioning. Several sections include examples of confronting models with data. Chapter summaries and problem sets at the end of each chapter provide opportunities to evaluate and enrich ones understanding of the ecological ideas that each chapter introduces. R is rapidly becoming the lingua franca of quantitative sciences, and this text provides a tractable introduction to using the R programming environment in ecology. An appendix provides a general ...

-Titulo Original : All Of Statistics A Concise Course In Statistical Inference (springer Texts In Statistics)-Fabricante : Springer-Descripcion Original: Taken literally, the title All of Statistics is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data. Review Winner of the 2005 DeGroot Prize. From the reviews: Presuming no previous background in statistics and described by the author as demanding yet understandable because the material is as intuitive as possible (p. viii), this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters. Technometrics, August 2004 This book should be seriously considered as a text for a theoretical statsitics course for non-majors, and perhaps even for majors...The coverage of emerging and important topics is timely and welcomed...you should have this book on your desk as a reference to nothing less than All of Statistics. Biometrics, December 2004 Although All of Statistics is an ambitious title, this book is a concise guide, as the subtitle suggests....I recommend it to anyone who has an interest in learning something new about statistical inference. There is something here for everyone. The American Statistician, May 2005 As the title of the book suggests, ‘All of Statistics’ covers a wide range of statistical topics. … The number of topics covered in this book is vast … . The greatest strength of this book is as a first point of reference for a wide range of statistical methods. … I would recommend this book as a useful and interesting introduction to a large number of statistical topics for non-statisticians and also as a useful reference book for practicing statisticians. (Matthew J. Langdon, Journal of Applied Statistics, Vol. 32 (1), January, 2005) This book was written specifically to give students a quick but sound understanding of modern statistics, and its coverage is very wide. … The book is extremely well done … . (N. R. Draper, Short Book Reviews, Vol. 24 (2), 2004) This is most definitely a book about mathematical statistics. It is full of theorems and proofs … . Presuming no previous background in statistics … this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters. (Eric R. Ziegel, Technometrics, Vol. 46 (3), August, 2004) The author points out that this book is for those who wish to learn probability and statistics quickly … . this book will serve as a guideline for instructors as to what should constitute a basic education in modern statistics. It introduces many modern topics … . Adequate references are provided at the end of each chapter which the instructor will be able to use profitably … . (Arup Bose, Sankhya, Vol. 66 (3), 2004) The amount of material that is covered in this book is impressive. … the explanations are generally clear and the wide range of techniques that are discussed makes it possible to include a diverse set of examples … . The worked examples are complemented with numerous theoretical and practical exercises … . is a very useful overview of many areas of modern statistics and as such will be very useful to readers who require such a survey. Library copies would also see plenty of use. (Stuart Barber, Journal of the Royal Statistical Society, Series A - Statistics in Society, Vol. 168 (1), 2...

-Titulo Original : Applied Predictive Modeling-Fabricante : Springer-Descripcion Original: Winner of the 2014 Technometrics Ziegel Prize for Outstanding Book 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 for each step of the process. The data sets and corresponding code are available in the books companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioners reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the books R package. Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge. Review There are a wide variety of books available on predictive analytics and data modeling around the web. Weve carefully selected the following 10 books, based on relevance, popularity, online ratings, and their ability to add value to your business. 1. Applied Predictive Modeling. (Timothy King, Business Intelligence Solutions Review, solutions-review , June, 2015) I used this as a supplement in teaching a data science course that I use a range of different resources because I need to cover working with data, model evaluation, and machine learning methods. The next time I teach this course, I will use only this book because it covers all of these aspects of the field. (Louis Luangkesorn, lugerpitt.blogspot , June, 2015) This is such a good book it has taken me a while to work through the book. All the while finding examples of why people should read the book. Well thought out examples with the R packages and example code. Take your time and work through this book. (Mary Anne, Cats and Dogs with Data, maryannedata , February, 2015) This monograph presents a very friendly, practical course on prediction techniques for regression and classification models. The authors are recognized experts in modeling and forecasting, as well as developers of R packages and statistical methodologies. It is a well-written book very useful to students and practitioners who need an immediate and helpful way to apply complex statistical techniques. (Stan Lipovetsky, Technometrics, Vol. 56 (3), August, 2014) There are hundreds of books that have something worthwhile to say about predictive modeling. However, in my judgment, Applied Predictive Modeling by Max Kuhn and Kjell Johnson (Springer 2013) ought to be at the very top of the reading list. They come across like coaches who really, really want you to be able to do this stuff. They write simply and with great clarity. Applied Predictive Modeling is a remarkable text. It is the succinct distillation of years of experience of two expert modelers. (Joseph Rickert, blog.revolutionanalytics , June, 2014) Review This strong, technical, hands-on treatment clearly spells out the concepts, and illustrates its themes tangibly with the language R, the most popular open source analytics solution. (Eric Siegel, Ph.D. Founder, Predictive Analytics World, Author, Predictive Analytics: T...

-Titulo Original : The Algorithm Design Manual-Fabricante : Springer-Descripcion Original: This newly expanded and updated second edition of the best-selling classic continues to take the mystery out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students.The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: * Doubles the tutorial material and exercises over the first edition * Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video* Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them * Includes several NEW war stories relating experiences from real-world applications * Provides up-to-date links leading to the very best algorithm implementations available in C, C, and Java. Review From the reviews of the second edition: “This is a detailed and timeless book that I keep reaching for. A useful book that I can not keep access to long enough. It is a important book to have in your library. Unlike other algorithm books that I have the algorithms are written independent of any programming language.” (Mary Anne, Cats and Dogs with Data, maryannedata , April, 2014) “The Algorithm Design Manual by Steven Skiena is aimed at two groups of people: students and professionals. … It is written in an informal style that I found pleasant and engaging. … The book’s unique structure makes it more likely to be immediately useful to the practitioner who has problem to solve and wants to quickly make progress. The book succeeds admirably. Would be helpful to the student who has never seen this material before. Overall, I recommend this book warmly.” (Neelakantan Kartha, The Book Review Column, 2011) “Algorithms are the very heart of computing. This book is about right for most people. Each of the topics is treated in a readable informal style with lots of asides and accounts of personal experiences - ‘war stories’ in implementing algorithms. If you want to use it as a course textbook then there are lots of exercises at the end of every chapter. Highly recommended.” (Mike James, I Programmer, September, 2009) Addressing the main difficulties of solving problems, this book goes far beyond the design of algorithms. It is essential for scientists, engineers, and any professionals who aim to solve problems, with a noticeable emphasis on real problems. It will not only serve as a valuable undergraduate textbook, but it will also become an irreplaceable reference guide for most professionals in the area. (Carlos Linares Lopez, Computing Reviews, February, 2009) Skiena focuses on the practical aspects of algorithm design and use. This work fills an important gap in the knowledge of CS practitioners and students. This book is suitable as a text for an undergraduate algorithms class, but also as an invaluable reference for the practicing programmer. This second edition has updated the bibliography to include recent works, making it an extensive bibliography. The index is also thorough and very useful for finding specific problems. (William Fahle, ACM Computing Reviews, December, 2008) For a decade, Steven Skiena’s Algorithm Design Manual retained its title as the best and most comprehensive practical algorithm guide to help identify and solve problems. It...

-Titulo Original : An Introduction To Kolmogorov Complexity And Its Applications (texts In Computer Science)-Fabricante : Springer-Descripcion Original: This must-read textbook presents an essential introduction to Kolmogorov complexity (KC), a central theory and powerful tool in information science that deals with the quantity of information in individual objects. The text covers both the fundamental concepts and the most important practical applications, supported by a wealth of didactic features. This thoroughly revised and enhanced fourth edition includes new and updated material on, amongst other topics, the Miller-Yu theorem, the Gacs-Ku era theorem, the Day-Gacs theorem, increasing randomness, short lists computable from an input string containing the incomputable Kolmogorov complexity of the input, the Lovasz local lemma, sorting, the algorithmic full Slepian-Wolf theorem for individual strings, multiset normalized information distance and normalized web distance, and conditional universal distribution. Topics and features: describes the mathematical theory of KC, including the theories of algorithmic complexity and algorithmic probability; presents a general theory of inductive reasoning and its applications, and reviews the utility of the incompressibility method; covers the practical application of KC in great detail, including the normalized information distance (the similarity metric) and information diameter of multisets in phylogeny, language trees, music, heterogeneous files, and clustering; discusses the many applications of resource-bounded KC, and examines different physical theories from a KC point of view; includes numerous examples that elaborate the theory, and a range of exercises of varying difficulty (with solutions); offers explanatory asides on technical issues, and extensive historical sections; suggests structures for several one-semester courses in the preface. As the definitive textbook on Kolmogorov complexity, this comprehensive and self-contained work is an invaluable resource for advanced undergraduate students, graduate students, and researchers in all fields of science. Review This textbook An Introduction to Kolmogorov Complexity and Its Applications, 4th edition won the Textbook & Academic Authors Association (TAA) McGuffey Longevity Award in 2020. From the Back Cover This must-read textbook presents an essential introduction to Kolmogorov complexity (KC), a central theory and powerful tool in information science that deals with the quantity of information in individual objects. The text covers both the fundamental concepts and the most important practical applications, supported by a wealth of didactic features. This thoroughly revised and enhanced fourth edition includes new and updated material on, amongst other topics, the Miller-Yu theorem, the Gacs-Ku era theorem, the Day-Gacs theorem, increasing randomness, short lists computable from an input string containing the incomputable Kolmogorov complexity of the input, the Lovasz local lemma, sorting, the algorithmic full Slepian-Wolf theorem for individual strings, multiset normalized information distance and normalized web distance, and conditional universal distribution. Topics and features: Describes the mathematical theory of KC, including the theories of algorithmic complexity and algorithmic probability Presents a general theory of inductive reasoning and its applications, and reviews the utility of the incompressibility method Covers the practical application of KC in great detail, including the normalized information distance (the similarity metric) and information diameter of multisets in phylogeny, language trees, music, heterogeneous files, and clustering Discusses the many applications of resource-bounded KC, and examines different physical theories from a KC point of view Includes numerous examples that elaborate the theory, and a range of exercises of varying difficulty (with solutions) Offers explanatory asides on technical issues, and extensive historical sections Suggests structures for several one-semester cour...

-Titulo Original : Introduction To Software Design With Java-Fabricante : Springer-Descripcion Original: This textbook provides an in-depth introduction to software design, with a focus on object-oriented design, and using the Java programming language. Its goal is to help readers learn software design by discovering the experience of the design process. To this end, a narrative is used that introduces each element of design know-how in context, and explores alternative solutions in that context. The narrative is supported by hundreds of code fragments and design diagrams. The first chapter is a general introduction to software design. The subsequent chapters cover design concepts and techniques, which are presented as a continuous narrative anchored in specific design problems. The design concepts and techniques covered include effective use of types and interfaces, encapsulation, composition, inheritance, design patterns, unit testing, and many more. A major emphasis is placed on coding and experimentation as a necessary complement to reading the text. To support this aspect of the learning process, a companion website with practice problems is provided, and three sample applications that capture numerous design decisions are included. Guidance on these sample applications is provided in a section called “Code Exploration” at the end of each chapter. Although the Java language is used as a means of conveying design-related ideas, the book’s main goal is to address concepts and techniques that are applicable in a host of technologies. This book is intended for readers who have a minimum of programming experience and want to move from writing small programs and scripts to tackling the development of larger systems. This audience naturally includes students in university-level computer science and software engineering programs. As the prerequisites to specific computing concepts are kept to a minimum, the content is also accessible to programmers without a primary training in computing. In a similar vein, understanding the code fragments requires only a minimal grasp of the language, such as would be taught in an introductory programming course. From the Back Cover This textbook provides an in-depth introduction to software design, with a focus on object-oriented design, and using the Java programming language. Its goal is to help readers learn software design by discovering the experience of the design process. To this end, a narrative is used that introduces each element of design know-how in context, and explores alternative solutions in that context. The narrative is supported by hundreds of code fragments and design diagrams. The first chapter is a general introduction to software design. The subsequent chapters cover design concepts and techniques, which are presented as a continuous narrative anchored in specific design problems. The design concepts and techniques covered include effective use of types and interfaces, encapsulation, composition, inheritance, design patterns, unit testing, and many more. A major emphasis is placed on coding and experimentation as a necessary complement to reading the text. To support this aspect of the learning process, a companion website with practice problems is provided, and three sample applications that capture numerous design decisions are included. Guidance on these sample applications is provided in a section called “Code Exploration” at the end of each chapter. Although the Java language is used as a means of conveying design-related ideas, the book’s main goal is to address concepts and techniques that are applicable in a host of technologies. This book is intended for readers who have a minimum of programming experience and want to move from writing small programs and scripts to tackling the development of larger systems. This audience naturally includes students in university-level computer science and software engineering programs. As the prerequisites to specific computing concepts are kept to a minimum, the content...

-Titulo Original : Introduction To The Foundations Of Applied Mathematics (texts In Applied Mathematics, 56)-Fabricante : Springer-Descripcion Original: The objective of this textbook is the construction, analysis, and interpretation of mathematical models to help us understand the world we live in. Rather than follow a case study approach it develops the mathematical and physical ideas that are fundamental in understanding contemporary problems in science and engineering. Science evolves, and this means that the problems of current interest continually change. What does not change as quickly is the approach used to derive the relevant mathematical models, and the methods used to analyze the models. Consequently, this book is written in such a way as to establish the mathematical ideas underlying model development independently of a specific application. This does not mean applications are not considered, they are, and connections with experiment are a staple of this book. The book, as well as the individual chapters, is written in such a way that the material becomes more sophisticated as you progress. This provides some flexibility in how the book is used, allowing consideration for the breadth and depth of the material covered. Moreover, there are a wide spectrum of exercises and detailed illustrations that significantly enrich the material. Students and researchers interested in mathematical modelling in mathematics, physics, engineering and the applied sciences will find this text useful. The material, and topics, have been updated to include recent developments in mathematical modeling. The exercises have also been expanded to include these changes, as well as enhance those from the first edition. Review of first edition: The goal of this book is to introduce the mathematical tools needed for analyzing and deriving mathematical models. … Holmes is able to integrate the theory with application in a very nice way providing an excellent book on applied mathematics. … One of the best features of the book is the abundant number of exercises found at the end of each chapter. … I think this is a great book, and I recommend it for scholarly purposes by students, teachers, and researchers. Joe Latulippe, The Mathematical Association of America, December, 2009 From the Back Cover The objective of this textbook is the construction, analysis, and interpretation of mathematical models to help us understand the world we live in. Rather than follow a case study approach it develops the mathematical and physical ideas that are fundamental in understanding contemporary problems in science and engineering. Science evolves, and this means that the problems of current interest continually change. What does not change as quickly is the approach used to derive the relevant mathematical models, and the methods used to analyze the models. Consequently, this book is written in such a way as to establish the mathematical ideas underlying model development independently of a specific application. This does not mean applications are not considered, they are, and connections with experiment are a staple of this book. The book, as well as the individual chapters, is written in such a way that the material becomes more sophisticated as you progress. This provides some flexibility in how the book is used, allowing consideration for the breadth and depth of the material covered. Moreover, there are a wide spectrum of exercises and detailed illustrations that significantly enrich the material. Students and researchers interested in mathematical modelling in mathematics, physics, engineering and the applied sciences will find this text useful. The material, and topics, have been updated to include recent developments in mathematical modeling. The exercises have also been expanded to include these changes, as well as enhance those from the first edition. Review of first edition:The goal of this book is to introduce the mathematical tools needed for analyzing and deriving mathematical models. … Holmes is able to integrate the theory with application in a ver...

-Titulo Original : Data Visualisation With R 111 Examples-Fabricante : Springer-Descripcion Original: This book introduces readers to the fundamentals of creating presentation graphics using R, based on 111 detailed and complete scripts. It shows how bar and column charts, population pyramids, Lorenz curves, box plots, scatter plots, time series, radial polygons, Gantt charts, heat maps, bump charts, mosaic and balloon charts, and a series of different thematic map types can be created using R’s Base Graphics System. Every example uses real data and includes step-by-step explanations of the figures and their programming. This second edition contains additional examples for cartograms, chord-diagrams and networks, and interactive visualizations with Javascript. The open source software R is an established standard and a powerful tool for various visualizing applications, integrating nearly all technologies relevant for data visualization. The basic software, enhanced by more than 14000 extension packs currently freely available, is intensively used by organizations including Google, and the CIA. The book serves as a comprehensive reference guide to a broad variety of applications in various fields. This book is intended for all kinds of R users, ranging from experts, for whom especially the example codes are particularly useful, to beginners, who will find the finished graphics most helpful in learning what R can actually deliver. Review “The book can be one of the favourites of a wide range of users from beginners with basic R knowledge to experts. It is especially recommended for students and researchers from social, environmental, and economic fields requiring a consistent and thorough reference always within reach.” (Marta Ladanyi, ISCB News, iscb.info, Issue 69, July, 2020) Review “Provides the reader with a broad and excellent overview of the various possibilities offered by R’s standard graphics package. … Overall, the book can especially be recommended for students and researchers that are already familiar with R and who intend to find suitable methods for visualizing their data in real world applications. Furthermore, it can also be helpful for students who want a broad overview of R and its visualization capabilities.” (Marc Husch, Statistical Papers, April, 2018) “I definitely recommend this book. The example-based approach is very successful for introducing readers to R’s graphical capabilities; readers can learn proficiency in using base R graphics for obtaining exactly the static presentation figure they envision - including ambitious infographics. … Last but (by far) not least, the book can be used as a collection of ideas for useful, informative and beautiful graphical displays.” (Ulrike Gromping, Journal of Statistical Software, Vol. 81, October, 2017) “This is a well written book for designers. … Enjoy this book. I am having fun getting the code to work on other data.” (Mary Anne Thygesen, Cats and Dogs with Data, maryannedata , May, 2017) “If you’ve ever been interested in coding using R, then this book is for you. … If like me, you need hands-on guidance the examples (which use real data) are shown with a step-by-step walkthrough teaching you everything that you need to know.” (Sandro Sorrentino, Medium, medium , February, 2017) “I really like this book: those 100 interesting real data examples with the detailed R-scripts for producing beautiful graphical visualisation are particularly helpful for learning R graphics. I will use it as a main textbook for data visualization for my statistics course.” (Professor Qiwei Yao, Department of Statistics, London School of Economics) “I like the book. With my two doctoral course students, I use it as a text book for my ‚Doctoral Dissertation Research‘ course in this semester.“ (Prof Dae-Heung Jang, Department of Statistics, Pukyong National University, Busan, South Korea, 2017) From the Back Cover This book introduces readers to the fundamentals of creating presentation graphics using R...

-Titulo Original : Data Mining With Spss Modeler Theory, Exercises And Solutions-Fabricante : Springer-Descripcion Original: Now in its second edition, this textbook introduces readers to the IBM SPSS Modeler and guides them through data mining processes and relevant statistical methods. Focusing on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs, it also features a variety of exercises and solutions, as well as an accompanying website with data sets and SPSS Modeler streams. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice. This revised and updated second edition includes a new chapter on imbalanced data and resampling techniques as well as an extensive case study on the cross-industry standard process for data mining. From the Back Cover Now in its second edition, this textbook introduces readers to the IBM SPSS Modeler and guides them through data mining processes and relevant statistical methods. Focusing on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs, it also features a variety of exercises and solutions, as well as an accompanying website with data sets and SPSS Modeler streams. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice. This revised and updated second edition includes a new chapter on imbalanced data and resampling techniques as well as an extensive case study on the cross-industry standard process for data mining. About the Author Dr. Tilo Wendler is a Professor at the University of Applied Sciences HTW Berlin, Germany. He studied mathematics, physics and business information technology. In his doctoral thesis, he examined determinants of user expectations in using information technology. He is also interested in applying complex statistical methods in the banking sector, especially in the field of rating methods. He has been teaching business statistics and data mining for ten years. Dr. Soren Grottrup is a Professor of Machine Learning and Statistics at the Technische Hochschule Ingolstadt, Germany. After studying mathematics and computer science, he was awarded a Ph.D. for his research on stochastic models with biological applications. Alongside his doctoral studies, he worked as a data analyst, analyzing genomic data at a research institute. For many years, he worked as a data scientist in the airline and data-driven marketing business, and advised various companies on digitalization and machine learning projects...

-Titulo Original : Digital Libraries For Open Knowledge 24th International Conference On Theory And Practice Of Digital Libraries, Tpdl 2020, Lyon, France, August ... (lecture Notes In Computer Science, 12246)-Fabricante : Springer-Descripcion Original: This book constitutes the proceedings of the 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, held in Lyon, France, in August 2020.* The 14 full papers and 4 short papers presented were carefully reviewed and selected from 53 submissions. TPDL 2020 attempts to facilitate establishing connections and convergences between diverse research communities such as Digital Humanities, Information Sciences and others that could benefit from ecosystems offered by digital libraries and repositories. The papers present a wide range of the following topics: knowledge graphs and linked data; quality assurance in digital libraries; ontology design; user requirements and behavior; research data management and discovery; and digital cultural heritage. * The conference was held virtually due to the COVID-19 pandemic...

-Titulo Original : Numerical Python In Astronomy And Astrophysics A Practical Guide To Astrophysical Problem Solving (undergraduate Lecture Notes In Physics)-Fabricante : Springer-Descripcion Original: This book provides a solid foundation in the Python programming language, numerical methods, and data analysis, all embedded within the context of astronomy and astrophysics. It not only enables students to learn programming with the aid of examples from these fields but also provides ample motivation for engagement in independent research. The book opens by outlining the importance of computational methods and programming algorithms in contemporary astronomical and astrophysical research, showing why programming in Python is a good choice for beginners. The performance of basic calculations with Python is then explained with reference to, for example, Kepler’s laws of planetary motion and gravitational and tidal forces. Here, essential background knowledge is provided as necessary. Subsequent chapters are designed to teach the reader to define and use important functions in Python and to utilize numerical methods to solve differential equations and landmark dynamical problems in astrophysics. Finally, the analysis of astronomical data is discussed, with various hands-on examples as well as guidance on astronomical image analysis and applications of artificial neural networks. From the Back Cover This book provides a solid foundation in the Python programming language, numerical methods, and data analysis, all embedded within the context of astronomy and astrophysics. It not only enables students to learn programming with the aid of examples from these fields but also provides ample motivation for engagement in independent research. The book opens by outlining the importance of computational methods and programming algorithms in contemporary astronomical and astrophysical research, showing why programming in Python is a good choice for beginners. The performance of basic calculations with Python is then explained with reference to, for example, Kepler’s laws of planetary motion and gravitational and tidal forces. Here, essential background knowledge is provided as necessary. Subsequent chapters are designed to teach the reader to define and use important functions in Python and to utilize numerical methods to solve differential equations and landmark dynamical problems in astrophysics. Finally, the analysis of astronomical data is discussed, with various hands-on examples as well as guidance on astronomical image analysis and applications of artificial neural networks. About the Author Wolfram Schmidt is Head of IT and senior researcher at the Hamburg Observatory. He holds a Dipl.-Ing. in Physics and an M.Phil. in Astrophysics and received his Dr. rer. nat. from the Technical University of Munich and the Max Planck Institute for Astrophysics, Garching. He completed his Habilitation with a thesis on the numerical modeling of astrophysical turbulence. Dr. Schmidt has worked on numerical simulations of a variety of astrophysical systems, ranging from thermonuclear supernovae through star-forming clouds to cosmological structure formation. Currently he is conducting research projects in the areas of computational cosmology and magnetohydrodynamic turbulence with international collaborators. In addition to his research activities, he teaches high-performance computing at Hamburg University and acts as a consultant to the Northern Supercomputing Alliance (HLRN). Marcel Volschow is a research associate at Hamburg University. He holds BSc and MSc degrees in Physics and the subject of his doctorate is magnetic processes in binary stars. While being rooted in theoretical astrophysics, his research also covers the processing and analysis of large survey data sets. Whether working in public outreach, high school labs, or academic teaching, he has always been motivated by enthusiasm for the beauty of the universe. He is the author of a number of articles in peer-reviewed journals...

-Titulo Original : Machine Learning With Quantum Computers (quantum Science And Technology)-Fabricante : Springer-Descripcion Original: This book offers an introduction into quantum machine learning research, covering approaches that range from near-term to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years. From the Back Cover This book offers an introduction into quantum machine learning research, covering approaches that range from near-term to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years. About the Author Maria Schuld works as a researcher for the Toronto-based quantum computing start-up Xanadu. She received her Ph.D. from the University of KwaZulu-Natal in 2017, where she began working on the intersection between quantum computing and machine learning in 2013. Besides her numerous contributions to the field, she is a co-developer for the open-source quantum machine learning software framework PennyLane. Francesco Petruccione received his Ph.D. (1988) and “Habilitation” (1994) from the University of Freiburg, Germany. Since 2004, he has been a professor of Theoretical Physics at the University of KwaZulu-Natal in Durban, South Africa, where in 2007, he was granted a South African Research Chair for Quantum Information Processing and Communication. He is the co-author of “The Theory of Open Quantum Systems” (Oxford University Press, 2002) and has published more than 250 papers in refereed journals. Francesco Petruccione’s research focuses on open quantum systems and quantum information processing and communication...

-Titulo Original : Computer Security And The Internet Tools And Jewels From Malware To Bitcoin (information Security And Cryptography)-Fabricante : Springer-Descripcion Original: This book provides a concise yet comprehensive overview of computer and Internet security, suitable for a one-term introductory course for junior/senior undergrad or first-year graduate students. It is also suitable for self-study by anyone seeking a solid footing in security - including software developers and computing professionals, technical managers and government staff. An overriding focus is on brevity, without sacrificing breadth of core topics or technical detail within them. The aim is to enable a broad understanding in roughly 350 pages. Further prioritization is supported by designating as optional selected content within this. Fundamental academic concepts are reinforced by specifics and examples, and related to applied problems and real-world incidents. The first chapter provides a gentle overview and 20 design principles for security. The ten chapters that follow provide a framework for understanding computer and Internet security. They regularly refer back to the principles, with supporting examples. These principles are the conceptual counterparts of security-related error patterns that have been recurring in software and system designs for over 50 years. The book is “elementary” in that it assumes no background in security, but unlike “soft” high-level texts it does not avoid low-level details, instead it selectively dives into fine points for exemplary topics to concretely illustrate concepts and principles. The book is rigorous in the sense of being technically sound, but avoids both mathematical proofs and lengthy source-code examples that typically make books inaccessible to general audiences. Knowledge of elementary operating system and networking concepts is helpful, but review sections summarize the essential background. For graduate students, inline exercises and supplemental references provided in per-chapter endnotes provide a bridge to further topics and a springboard to the research literature; for those in industry and government, pointers are provided to helpful surveys and relevant standards, e.g., documents from the Internet Engineering Task Force (IETF), and the U.S. National Institute of Standards and Technology. Review [This] book identifies ten relatively self-contained structural areas of widespread concern, each of which is probed with detail sufficient to establish relatively accessible groundwork for the primary concepts. The book makes considerable headway into underlying realities that otherwise tend to make things difficult to understand, to design, and to implement correctly. It provides collected wisdom on how to overcome complexity in many critical areas, and forms a sound basis for many of the necessary fundamental com­ponents and concepts. Also, much of what is described here is well chosen, because it has survived the test of time … Understanding everything in this book is an essential precursor to achieving meaningfully trustworthy systems … Paul’s realistic approach and structural organization in this book are likely to provide a very useful early step - particularly for students and emerging practitioners, but also for computer users interested in a better understanding of what attaining security might entail. [Peter G. Neumann, SRI International] This is THE textbook weve been waiting for when redesigning our introductory computer security course! Well-balanced emphasis on the right issues, getting to the bone where necessary. Pauls book filled in the gap we identified quite some time ago. [Vashek Matyaš, Masaryk University]Extremely clear and well-organized. The best introduction to the field. [Cormac Herley, Microsoft Research] This books combination of grounding and theory addresses an important gap, and will serve new stu­dents well! [Adam Shostack, author of “Threat Modeling: Designing for Security”] Paul’s book does an amazing job of distilling the chaotic panoply of the security world into a remarkably coherent,...

-Titulo Original : Information Visualization: An Introduction-Fabricante : Springer-Descripcion Original: Information visualization is the act of gaining insight into data, and is carried out by virtually everyone. It is usually facilitated by turning data - often a collection of numbers - into images that allow much easier comprehension. Everyone benefits from information visualization, whether internet shopping, investigating fraud or indulging an interest in art. So no assumptions are made about specialist background knowledge in, for example, computer science, mathematics, programming or human cognition. Indeed, the book is directed at two main audiences. One comprises first year students of any discipline. The other comprises graduates - again of any discipline - who are taking a one- or two-year course of training to be visual and interaction designers. By focusing on the activity of design the pedagogical approach adopted by the book is based on the view that the best way to learn about the subject is to do it, to be creative: not to prepare for the ubiquitous examination paper. The content of the book, and the associated exercises, are typically used to support five creative design exercises, the final one being a group project mirroring the activity of a consultancy undertaking a design (not an implementation) for a client. Engagement with the material of this book can have a variety of outcomes. The composer of a school newsletter and the applicant for a multi-million investment should both be able to convey their message more effectively, and the curator of an exhibition will have new presentational techniques on their palette. For those students training to be visual/interaction designers the exercises have led to original and stimulating outcomes. Review “The book itself is a very good introduction to information visualization, and as such, it can serve for an introductory course allowing various pedagogical paths for an instructor to take. … it is a very readable book to be recommended not only to prospective students of information visualization, but to anyone interested in the topic, since it is written in such a way that there are no prerequisites to be able to read it.” (P. Navrat, Computing Reviews, July, 2015) “The title is accurate. There is a minor genre of books on this topic within statistics. This book differs in focusing on a broader range of data types. It also gives more attention to psychological and cognitive issues. … Professionals working primarily on interface design for a broad audience, say map or web page designers, may find the text useful as it discusses many issues other books on visualization ignore.” (Robert W. Hayden, MAA Reviews, June, 2015) From the Back Cover Information visualization is the act of gaining insight into data, and is carried out by virtually everyone. It is usually facilitated by turning data - often a collection of numbers - into images that allow much easier comprehension. Everyone benefits from information visualization, whether internet shopping, investigating fraud or indulging an interest in art. So no assumptions are made about specialist background knowledge in, for example, computer science, mathematics, programming or human cognition. Indeed, the book is directed at two main audiences. One comprises first year students of any discipline. The other comprises graduates - again of any discipline - who are taking a one- or two-year course of training to be visual and interaction designers. By focusing on the activity of design the pedagogical approach adopted by the book is based on the view that the best way to learn about the subject is to do it, to be creative: not to prepare for the ubiquitous examination paper. The content of the book, and the associated exercises, are typically used to support five creative design exercises, the final one being a group project mirroring the activity of a consultancy undertaking a design (not an implementation) for a client. Engagement with the material of this book can have...

-Titulo Original : Practical Analysis Of Algorithms (undergraduate Topics In Computer Science)-Fabricante : Springer-Descripcion Original: This book introduces the essential concepts of algorithm analysis required by core undergraduate and graduate computer science courses, in addition to providing a review of the fundamental mathematical notions necessary to understand these concepts. Features: includes numerous fully-worked examples and step-by-step proofs, assuming no strong mathematical background; describes the foundation of the analysis of algorithms theory in terms of the big-Oh, Omega, and Theta notations; examines recurrence relations; discusses the concepts of basic operation, traditional loop counting, and best case and worst case complexities; reviews various algorithms of a probabilistic nature, and uses elements of probability theory to compute the average complexity of algorithms such as Quicksort; introduces a variety of classical finite graph algorithms, together with an analysis of their complexity; provides an appendix on probability theory, reviewing the major definitions and theorems used in the book. Review “This textbook is a practical approach to the analysis of algorithms from a programmer’s perspective. … This book therefore does play an important role for educating programmers on how to analyze the runtime requirements of their algorithms. … The authors are in essence talking to readers when presenting the material and take a step-by-step approach when presenting the mathematical proofs. … A one-semester course on the analysis of algorithms can be crafted based on the chapters of this text.” (R. Goldberg, Computing Reviews, March, 2015) “Vrajitoru and Knight (both, Indiana Univ.) cover standard algorithmic problems from sorting to graph theory. The authors provide ready-to-run C programs for many of their algorithms. It is clear from the exercises and examples that this books content has been used in teaching undergraduates for a number of years. … Summing Up: Recommended. Lower- and upper-division undergraduates and faculty.” (P. Cull, Choice, Vol. 52 (10), June, 2015)“The book is dedicated to an old, important and complex topic in computer science. … The multiple exercises in each section invite the readers to a deeper exploration of the subjects. The formalized style of the book is expected to attract especially young students introduced already to mathematical formalisms. The material can be used as a textbook for undergraduate courses in computer science.” (Dana Petcu, zbMATH, Vol. 1303, 2015) From the Back Cover Analysis of algorithms plays an essential role in the education and training of any serious programmer preparing to deal with real world applications. Practical Analysis of Algorithms introduces the essential concepts of algorithm analysis required by core undergraduate and graduate computer science courses, in addition to providing a review of the fundamental mathematical notions necessary to understand these concepts. Throughout the text, the explanations are aimed at the level of understanding of a typical upper-level student, and are accompanied by detailed examples and classroom-tested exercises. Topics and features: Includes numerous fully-worked examples and step-by-step proofs, assuming no strong mathematical background Describes the foundation of the analysis of algorithms theory in terms of the big-Oh, Omega, and Theta notations Examines recurrence relations, a very important tool used in the analysis of algorithms Discusses the concepts of basic operation, traditional loop counting, and best case and worst case complexities Reviews various algorithms of a probabilistic nature, and uses elements of probability theory to compute the average complexity of algorithms such as Quicksort Introduces a variety of classical finite graph algorithms, together with an analysis of their complexity Provides an appendix on probability theory, reviewing the major definitions and theorems used in the book This clearly-structured and easy-to-read textbook/reference applies a ...

-Titulo Original : Ggplot2 Elegant Graphics For Data Analysis (use R)-Fabricante : Springer-Descripcion Original: This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, its easy to: produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and youll learn everything you need in the book. After reading this book youll be able to produce graphics customized precisely for your problems, and youll find it easy to get graphics out of your head and on to the screen or page. Review “The versatility and efficiency of ggplot have led to the development of ggplot2 and this book which overviews the standard use and presentation secrets of functions developed in the last 5 years. The book is written in an accessible manner and it is suitable for undergraduates, postgraduates and researchers with some R experience. All theoretical concepts are accompanied by code, making it easy to learn by reproducing the examples.” (Irina Ioana Mohorianu, zbMATH 1397.62006, 2018) “The book is an excellent and very comprehensive manual of one of the most popular R packages. It is currently the only book describing ggplot2 in such depth. The book contains many examples and is very nicely illustrated, demonstrating the strength of the package.” (Klaus Galensa, Computing Reviews, May, 2017) From the Back Cover This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, its easy to: * produce handsome, publication-quality plots with automatic legends created from the plot specification * superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales * add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression * save any ggplot2 plot (or part thereof) for later modification or reuse * create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots * approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and youll learn everything you need in the book. After reading this book youll be able to produce graphics customized precisely for your problems, and youll find it easy to get graphics out of your head and on to the screen or page. New to this edition: * Brings the book up-to-date with ggplot2 1.0, including major ...

-Titulo Original : Recommender Systems The Textbook-Fabricante : Springer-Descripcion Original: This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors. Review “Charu Aggarwal, a well-known, reputable IBM researcher, has taken the time to distill the advances in the design of recommender systems since the advent of the web … . Extensive bibliographic notes at the end of each chapter and more than 700 references in the book bibliography make this monograph an excellent resource for both practitioners and researchers. … Without a doubt, this is an excellent addition to my bookshelf!” (Fernando Berzal, Computing Reviews, February, 2017) From the Back Cover This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: - Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation.- Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. - Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications.Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors. About the Author: Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at t...