The.Hottest

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani ... 426 pages - Publisher: Springer; (September, 2017) ... Language: English - ISBN-10: 1461471370 - ISBN-13: 978-1461471370.

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

Brian Steele, John Chandler, Swarna Reddy ... 430 pages - Publisher: Springer; 1st edition (January, 2017) ... Language: English - ISBN-10: 3319457950 - ISBN-13: 978-3319457956 ...

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts: (a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter. (b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System. (c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

Mohssen Mohammed,‎ Muhammad Badruddin Khan,‎ Eihab Bashier Mohammed Bashier ... 226 pages - Publisher: CRC Press; 1st edition (July, 2016) ... Language: English - ISBN-10: 1498705383 - ISBN-13: 978-1498705387 ...

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.

Ronald L. Graham,‎ Donald E. Knuth,‎ Oren Patashnik ... 672 pages - Publisher: Addison-Wesley Professional; 2nd edition (March, 1994) ... Language: English - ISBN-10: 0201558025 - ISBN-13: 978-0201558029.

This book introduces the mathematics that supports advanced computer programming and the analysis of algorithms. The primary aim of its well-known authors is to provide a solid and relevant base of mathematical skills - the skills needed to solve complex problems, to evaluate horrendous sums, and to discover subtle patterns in data. It is an indispensable text and reference not only for computer scientists - the authors themselves rely heavily on it! - but for serious users of mathematics in virtually every discipline. Concrete Mathematics is a blending of CONtinuous and disCRETE mathematics. "More concretely," the authors explain, "it is the controlled manipulation of mathematical formulas, using a collection of techniques for solving problems." The subject matter is primarily an expansion of the Mathematical Preliminaries section in Knuth's classic Art of Computer Programming, but the style of presentation is more leisurely, and individual topics are covered more deeply. Several new topics have been added, and the most significant ideas have been traced to their historical roots. The book includes more than 500 exercises, divided into six categories. Complete answers are provided for all exercises, except research problems, making the book particularly valuable for self-study. Major topics include: Sums * Recurrences * Integer functions * Elementary number theory * Binomial coefficients * Generating functions * Discrete probability * Asymptotic methods. This second edition includes important new material about mechanical summation. In response to the widespread use of the first edition as a reference book, the bibliography and index have also been expanded, and additional nontrivial improvements can be found on almost every page. Readers will appreciate the informal style of Concrete Mathematics. Particularly enjoyable are the marginal graffiti contributed by students who have taken courses based on this material. The authors want to convey not only the importance of the techniques presented, but some of the fun in learning and using them.

IBM SPSS Statistics v25 [Size: 722 MB] ... IBM SPSS Statistics Subscription delivers the power of SPSS Statistics predictive analytics capabilities with a flexible subscription payment option, including an easier purchasing, management and licensing experience. With the introduction of SPSS Statistics Subscription, organizations, groups and individuals can take advantage of predictive analytics capabilities to deliver the maximum amount of value to the user. Propel research & analysis with a fast and powerful solution: A comprehensive set of statistical tools - Work inside a single, integrated interface to run descriptive statistics, regression, advanced statistics and many more. Create publication ready charts, tables, and decision trees in one tool. - Integration with Open Source - Enhance the SPSS Syntax with R and Python through specialized extensions. Leverage the 130+ extensions available on our Extension Hub, or build your own and share with your peers to create a customized solution. - Easy statistical analysis - Use a simple drag and drop interface to access a wide range of capabilities and work across multiple data sources. Plus, flexible deployment options make purchasing and managing your software easy. Subscription Base: Data Preparation - IBM SPSS Data Preparation is a fully integrated module of IBM SPSS Statistics. - Bootstrapping - Bootstrapping is a useful technique for testing model stability. Custom Tables and Advanced Statistics Add-On: Advanced Statistics - Improve the accuracy of your analysis and the dependability of your conclusions with statistics designed for complex relationships. - Regression - IBM SPSS Regression enables you to predict categorical outcomes and apply various nonlinear regression procedures. - Custom Tables: IBM SPSS Custom Tables enables you to summarize SPSS Statistics data, and display your analyses as presentation-quality, production-ready tables.

GeoStru Liquiter 2018.18.4.448 [Size: 47 MB] ... Liquiter software is designed for soil liquefaction analysis and supports a wide variety of field tests. The results of the analysis are presented as: Safety Factor Soil Liquefaction / Probability of Soil Liquefaction; Cyclic mobility of clay; Liquefaction of sand and clay; CSR, variable CSR with depth from SHAKE results; Reconsolidation Settlement, Lateral Spreading; Residual Strength.

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