A Hands-On Introduction to Data Science, Chirag Shah
Автор: Sutton Richard S., Barto Andrew G. Название: Reinforcement Learning: An Introduction, 2 ed. ISBN: 0262039249 ISBN-13(EAN): 9780262039246 Издательство: MIT Press Рейтинг: Цена: 18850.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
Like the first edition, this second edition focuses on core, online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new for the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Автор: Jeffrey Hoffstein and Jill Pipher Название: Introduction to Mathematical Cryptography ISBN: 1493917102 ISBN-13(EAN): 9781493917105 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Mathematical Cryptography
Автор: Saltz Jeffrey S., Stanton Jeffrey M. Название: An Introduction to Data Science ISBN: 150637753X ISBN-13(EAN): 9781506377537 Издательство: Sage Publications Рейтинг: Цена: 13306.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout.
Автор: Insurance Data Management Association (idma) Название: Introduction to data management functions and tools ISBN: 1634622499 ISBN-13(EAN): 9781634622493 Издательство: Gazelle Book Services Рейтинг: Цена: 17587.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This study guide is for the IDMA 201 course in the IDMA Associate Insurance Data Manager (AIDM) designation program. This course defines data management, describes the functions of data managers, provides the business case for data management and introduces the student to concepts and tools used by data managers. Whether you are an actuary, a claims professional, business analyst, or almost any of the other key functions, knowledge of data management can help you do your job better and help you prepare, understand, and protect the raw material-the data-so critical to your organisation. IDMA courses, workshops, and forums are highly recommended for a broad audience including new hires, IT and data modeling professionals who want to broaden their knowledge of the business side of insurance data management, anyone who manages and governs data in the industry (statistical, or management information data), and anyone who needs to use or communicate good quality data/information -- from actuaries to underwriters, and claims and analytics professionals. Students who complete the four IDMA-developed courses and successfully pass the examinations are awarded an Associate Insurance Data Manager (AIDM) designation. The IDMA courses may be taken in any order; there are no prerequisites. However, the courses are numbered to indicate a recommended sequence. For details on the designation requirements, please refer to the IDMA Website at www.IDMA.org.
Автор: Insurance Data Management Association (idma) Название: Introduction to data management functions & tools ISBN: 1634622421 ISBN-13(EAN): 9781634622424 Издательство: Gazelle Book Services Рейтинг: Цена: 31744.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook is for the IDMA 201 course in the IDMA Associate Insurance Data Manager (AIDM) designation program. This course defines data management, describes the functions of data managers, provides the business case for data management and introduces the student to concepts and tools used by data managers. Whether you are an actuary, a claims professional, business analyst, or almost any of the other key functions, knowledge of data management can help you do your job better and help you prepare, understand, and protect the raw material -- the data -- so critical to your organisation. IDMA courses, workshops, and forums are highly recommended for a broad audience including new hires, IT and data modeling professionals who want to broaden their knowledge of the business side of insurance data management, anyone who manages and governs data in the industry (statistical, or management information data), and anyone who needs to use or communicate good quality data/information -- from actuaries to underwriters, and claims and analytics professionals. Students who complete the four IDMA-developed courses and successfully pass the examinations are awarded an Associate Insurance Data Manager (AIDM) designation. The IDMA courses may be taken in any order; there are no prerequisites. However, the courses are numbered to indicate a recommended sequence. For details on the designation requirements, please refer to the IDMA Website at www.IDMA.org.
Автор: S. Sumathi; S.N. Sivanandam Название: Introduction to Data Mining and its Applications ISBN: 3662500809 ISBN-13(EAN): 9783662500804 Издательство: Springer Рейтинг: Цена: 41787.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining.
Автор: Healy Kieran Название: Data Visualization: A Practical Introduction ISBN: 0691181624 ISBN-13(EAN): 9780691181622 Издательство: Wiley Рейтинг: Цена: 6653.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
An accessible primer on how to create effective graphics from data
This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way.
Data Visualization builds the reader's expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective "small multiple" plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible.
Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings.
Provides hands-on instruction using R and ggplot2
Shows how the "tidyverse" of data analysis tools makes working with R easier and more consistent
Includes a library of data sets, code, and functions
Автор: Agresti, Alan, Название: An Introduction to Categorical Data Analysis, 3rd Edition ISBN: 1119405262 ISBN-13(EAN): 9781119405269 Издательство: Wiley Рейтинг: Цена: 19317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A valuable new edition of a standard reference
The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data.
Adding to the value in the new edition is:
- Illustrations of the use of R software to perform all the analyses in the book
- A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis
- New sections in many chapters introducing the Bayesian approach for the methods of that chapter
- More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets
- An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises
Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more.
An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
Автор: Miroslav Kubat Название: An Introduction to Machine Learning ISBN: 3319876694 ISBN-13(EAN): 9783319876696 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Нет в наличии.
Описание: This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications.
Автор: Yang, Xin-She Название: Yang - INTRODUCTION TO ALGORITHMS FOR DATA MINING AND MAC... ISBN: 0128172169 ISBN-13(EAN): 9780128172162 Издательство: Elsevier Science Рейтинг: Цена: 9936.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning as well as optimization. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modelling skills so they can process and interpret data for classification, clustering, curve-fitting, and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.
Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics
Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study
Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages
Автор: Fung, Benjamin C.M. , Wang, Ke , Fu, Ada Wai-Che Название: Introduction to Privacy-Preserving Data Publishing ISBN: 0367383756 ISBN-13(EAN): 9780367383756 Издательство: Taylor&Francis Рейтинг: Цена: 9492.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques presents state-of-the-art information sharing and data integration methods that take into account privacy and data mining requirements.
The first part of the book discusses the fundamentals of the field. In the second part, the authors present anonymization methods for preserving information utility for specific data mining tasks. The third part examines the privacy issues, privacy models, and anonymization methods for realistic and challenging data publishing scenarios. While the first three parts focus on anonymizing relational data, the last part studies the privacy threats, privacy models, and anonymization methods for complex data, including transaction, trajectory, social network, and textual data.
This book not only explores privacy and information utility issues but also efficiency and scalability challenges. In many chapters, the authors highlight efficient and scalable methods and provide an analytical discussion to compare the strengths and weaknesses of different solutions.
Автор: M. Antonia Amaral Turkman, Carlos Daniel Paulino, Peter Muller Название: Computational Bayesian Statistics: An Introduction ISBN: 1108481035 ISBN-13(EAN): 9781108481038 Издательство: Cambridge Academ Рейтинг: Цена: 17424.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains the fundamental ideas of Bayesian analysis, with a focus on computational methods such as MCMC and available software such as R/R-INLA, OpenBUGS, JAGS, Stan, and BayesX. It is suitable as a textbook for a first graduate-level course and as a user`s guide for researchers and graduate students from beyond statistics.
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