# An Introduction To Statistical Learning

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## An Introduction to Statistical Learning

Author | : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani |

Publsiher | : Springer Nature |

Total Pages | : 607 |

Release | : 2021-07-29 |

ISBN | : 1071614185 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**An Introduction to Statistical Learning Book Excerpt:**

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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

## An Elementary Introduction to Statistical Learning Theory

Author | : Sanjeev Kulkarni,Gilbert Harman |

Publsiher | : John Wiley & Sons |

Total Pages | : 288 |

Release | : 2011-06-09 |

ISBN | : 9781118023464 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**An Elementary Introduction to Statistical Learning Theory Book Excerpt:**

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.

## Machine Learning and Data Science

Author | : Daniel D. Gutierrez |

Publsiher | : Unknown |

Total Pages | : 230 |

Release | : 2015-10-01 |

ISBN | : 9781634620963 |

Category | : Electronic Book |

Language | : EN, FR, DE, ES & NL |

**Machine Learning and Data Science Book Excerpt:**

This work provides the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation.

## Introduction to Statistical Machine Learning

Author | : Masashi Sugiyama |

Publsiher | : Morgan Kaufmann Publishers |

Total Pages | : 534 |

Release | : 2015-10-12 |

ISBN | : 9780128021217 |

Category | : Electronic Book |

Language | : EN, FR, DE, ES & NL |

**Introduction to Statistical Machine Learning Book Excerpt:**

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus. Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning. Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.

## A Computational Approach to Statistical Learning

Author | : Taylor Arnold,Michael Kane,Bryan W. Lewis |

Publsiher | : CRC Press |

Total Pages | : 362 |

Release | : 2019-01-23 |

ISBN | : 1351694766 |

Category | : Business & Economics |

Language | : EN, FR, DE, ES & NL |

**A Computational Approach to Statistical Learning Book Excerpt:**

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

## The Elements of Statistical Learning

Author | : Trevor Hastie,Robert Tibshirani,Jerome Friedman |

Publsiher | : Springer Science & Business Media |

Total Pages | : 536 |

Release | : 2013-11-11 |

ISBN | : 0387216065 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**The Elements of Statistical Learning Book Excerpt:**

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

## The Nature of Statistical Learning Theory

Author | : Vladimir Vapnik |

Publsiher | : Springer Science & Business Media |

Total Pages | : 314 |

Release | : 2013-06-29 |

ISBN | : 1475732643 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**The Nature of Statistical Learning Theory Book Excerpt:**

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

## An Elementary Introduction to Statistical Learning Theory

Author | : Sanjeev Kulkarni,Gilbert Harman |

Publsiher | : John Wiley & Sons |

Total Pages | : 0 |

Release | : 2011-06-09 |

ISBN | : 1118023463 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**An Elementary Introduction to Statistical Learning Theory Book Excerpt:**

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.

## Handbook of Quantitative Criminology

Author | : Alex R. Piquero,David Weisburd |

Publsiher | : Springer Science & Business Media |

Total Pages | : 787 |

Release | : 2009-12-16 |

ISBN | : 9780387776507 |

Category | : Social Science |

Language | : EN, FR, DE, ES & NL |

**Handbook of Quantitative Criminology Book Excerpt:**

Quantitative criminology has certainly come a long way since I was ?rst introduced to a largely qualitative criminology some 40 years ago, when I was recruited to lead a task force on science and technology for the President’s Commission on Law Enforcement and Administration of Justice. At that time, criminology was a very limited activity, depending almost exclusively on the Uniform Crime Reports (UCR) initiated by the FBI in 1929 for measurement of crime based on victim reports to the police and on police arrests. A ty- cal mode of analysis was simple bivariate correlation. Marvin Wolfgang and colleagues were makingan importantadvancebytrackinglongitudinaldata onarrestsin Philadelphia,an in- vation that was widely appreciated. And the ?eld was very small: I remember attending my ?rst meeting of the American Society of Criminology in about 1968 in an anteroom at New York University; there were about 25–30 people in attendance, mostly sociologists with a few lawyers thrown in. That Society today has over 3,000 members, mostly now drawn from criminology which has established its own clear identity, but augmented by a wide variety of disciplines that include statisticians, economists, demographers, and even a few engineers. This Handbook provides a remarkable testimony to the growth of that ?eld. Following the maxim that “if you can’t measure it, you can’t understand it,” we have seen the early dissatisfaction with the UCR replaced by a wide variety of new approaches to measuring crime victimization and offending.

## Biochemical and Molecular Basis of Pediatric Disease

Author | : Edward C.C. Wong,Dennis J. Dietzen,Michael J. Bennett,Shannon Haymond |

Publsiher | : Academic Press |

Total Pages | : 1168 |

Release | : 2021-05-13 |

ISBN | : 0128179635 |

Category | : Science |

Language | : EN, FR, DE, ES & NL |

**Biochemical and Molecular Basis of Pediatric Disease Book Excerpt:**

Biochemical and Molecular Basis of Pediatric Disease, Fifth Edition has been a well-respected reference in the field for decades. This revision continues the strong focus on understanding the pathogenesis of pediatric disease, emphasizing not only the important role of the clinical laboratory in defining parameters that change with the disease process, but also the molecular basis of many pediatric diseases. Provides a fully-updated resource with more color illustrations Focuses on the biochemical and molecular basis of disease as well as the analytical techniques Defines important differences in the pathophysiology of diseases, comparing childhood with adult

## Applied Univariate Bivariate and Multivariate Statistics Using Python

Author | : Daniel J. Denis |

Publsiher | : John Wiley & Sons |

Total Pages | : 304 |

Release | : 2021-07-14 |

ISBN | : 1119578183 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**Applied Univariate Bivariate and Multivariate Statistics Using Python Book Excerpt:**

Applied Univariate, Bivariate, and Multivariate Statistics Using Python A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in Python Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied. Most of the datasets used in the book are small enough to be easily entered into Python manually, though they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, making the book perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python. Readers will also benefit from the inclusion of: A review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors An introduction to Python, exploring how to communicate with Python A treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management An introduction to topics such as ANOVA, MANOVA and discriminant analysis, regression, principal components analysis, factor analysis, cluster analysis, among others, exploring the nature of what these techniques can vs. cannot do on a methodological level Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.

## Handbook of Research on Big Data Clustering and Machine Learning

Author | : Garcia Marquez, Fausto Pedro |

Publsiher | : IGI Global |

Total Pages | : 478 |

Release | : 2019-10-04 |

ISBN | : 1799801071 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Handbook of Research on Big Data Clustering and Machine Learning Book Excerpt:**

As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary. The Handbook of Research on Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.

## Nonparametric Statistics with Applications to Science and Engineering with R

Author | : Paul Kvam,Brani Vidakovic,Seong-joon Kim |

Publsiher | : John Wiley & Sons |

Total Pages | : 452 |

Release | : 2022-10-18 |

ISBN | : 1119268133 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**Nonparametric Statistics with Applications to Science and Engineering with R Book Excerpt:**

NONPARAMETRIC STATISTICS WITH APPLICATIONS TO SCIENCE AND ENGINEERING WITH R Introduction to the methods and techniques of traditional and modern nonparametric statistics, incorporating R code Nonparametric Statistics with Applications to Science and Engineering with R presents modern nonparametric statistics from a practical point of view, with the newly revised edition including custom R functions implementing nonparametric methods to explain how to compute them and make them more comprehensible. Relevant built-in functions and packages on CRAN are also provided with a sample code. R codes in the new edition not only enable readers to perform nonparametric analysis easily, but also to visualize and explore data using R’s powerful graphic systems, such as ggplot2 package and R base graphic system. The new edition includes useful tables at the end of each chapter that help the reader find data sets, files, functions, and packages that are used and relevant to the respective chapter. New examples and exercises that enable readers to gain a deeper insight into nonparametric statistics and increase their comprehension are also included. Some of the sample topics discussed in Nonparametric Statistics with Applications to Science and Engineering with R include: Basics of probability, statistics, Bayesian statistics, order statistics, Kolmogorov–Smirnov test statistics, rank tests, and designed experiments Categorical data, estimating distribution functions, density estimation, least squares regression, curve fitting techniques, wavelets, and bootstrap sampling EM algorithms, statistical learning, nonparametric Bayes, WinBUGS, properties of ranks, and Spearman coefficient of rank correlation Chi-square and goodness-of-fit, contingency tables, Fisher exact test, MC Nemar test, Cochran’s test, Mantel–Haenszel test, and Empirical Likelihood Nonparametric Statistics with Applications to Science and Engineering with R is a highly valuable resource for graduate students in engineering and the physical and mathematical sciences, as well as researchers who need a more comprehensive, but succinct understanding of modern nonparametric statistical methods.

## Statistical Foundations Reasoning and Inference

Author | : Göran Kauermann,Helmut Küchenhoff,Christian Heumann |

Publsiher | : Springer Nature |

Total Pages | : 361 |

Release | : 2021-09-30 |

ISBN | : 3030698270 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**Statistical Foundations Reasoning and Inference Book Excerpt:**

This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master’s students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.

## Information and Communication Technologies in Tourism 2018

Author | : Brigitte Stangl,Juho Pesonen |

Publsiher | : Springer |

Total Pages | : 542 |

Release | : 2017-12-22 |

ISBN | : 3319729233 |

Category | : Business & Economics |

Language | : EN, FR, DE, ES & NL |

**Information and Communication Technologies in Tourism 2018 Book Excerpt:**

This book presents the latest research into the application of information and communication technologies within the travel and tourism sectors. Readers will find insightful contributions on a wide range of topics, including digital marketing, social media and online travel reviews, mobile computing, augmented and virtual reality, gamification, recommender systems, electronic distribution, online education and learning, and the sharing economy. Particular attention is devoted to the actual and potential impact of big data, and the development and implementation of digital strategies, including digital marketing and the digital economy. In addition to the description of research advances and innovative concepts, a number of informative case studies are presented. The contents of the book are based on the 2018 ENTER eTourism conference, held in Jönköping, Sweden. The volume will appeal to all academics and practitioners with an interest in the most recent developments in eTourism.

## Machine Learning for Finance

Author | : Jannes Klaas |

Publsiher | : Packt Publishing Ltd |

Total Pages | : 456 |

Release | : 2019-05-30 |

ISBN | : 1789134692 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Machine Learning for Finance Book Excerpt:**

A guide to advances in machine learning for financial professionals, with working Python code Key FeaturesExplore advances in machine learning and how to put them to work in financial industriesClear explanation and expert discussion of how machine learning works, with an emphasis on financial applicationsDeep coverage of advanced machine learning approaches including neural networks, GANs, and reinforcement learningBook Description Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways. The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming. What you will learnApply machine learning to structured data, natural language, photographs, and written textHow machine learning can detect fraud, forecast financial trends, analyze customer sentiments, and moreImplement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlowDig deep into neural networks, examine uses of GANs and reinforcement learningDebug machine learning applications and prepare them for launchAddress bias and privacy concerns in machine learningWho this book is for This book is ideal for readers who understand math and Python, and want to adopt machine learning in financial applications. The book assumes college-level knowledge of math and statistics.

## Advances in Analytics and Applications

Author | : Arnab Kumar Laha |

Publsiher | : Springer |

Total Pages | : 297 |

Release | : 2018-09-07 |

ISBN | : 9811312087 |

Category | : Business & Economics |

Language | : EN, FR, DE, ES & NL |

**Advances in Analytics and Applications Book Excerpt:**

This book includes selected papers submitted to the ICADABAI-2017 conference, offering an overview of the new methodologies and presenting innovative applications that are of interest to both academicians and practitioners working in the area of analytics. It discusses predictive analytics applications, machine learning applications, human resource analytics, operations analytics, analytics in finance, methodology and econometric applications. The papers in the predictive analytics applications section discuss web analytics, email marketing, customer churn prediction, retail analytics and sports analytics. The section on machine learning applications then examines healthcare analytics, insurance analytics and machine analytics using different innovative machine learning techniques. Human resource analytics addresses important issues relating to talent acquisition and employability using analytics, while a paper in the section on operations analytics describe an innovative application in oil and gas industry. The papers in the analytics in finance part discuss the use of analytical tools in banking and commodity markets, and lastly the econometric applications part presents interesting banking and insurance applications.

## Data Science in Agriculture and Natural Resource Management

Author | : G. P. Obi Reddy,Mehul S. Raval,J. Adinarayana,Sanjay Chaudhary |

Publsiher | : Springer Nature |

Total Pages | : 316 |

Release | : 2021-10-11 |

ISBN | : 9811658471 |

Category | : Technology & Engineering |

Language | : EN, FR, DE, ES & NL |

**Data Science in Agriculture and Natural Resource Management Book Excerpt:**

This book aims to address emerging challenges in the field of agriculture and natural resource management using the principles and applications of data science (DS). The book is organized in three sections, and it has fourteen chapters dealing with specialized areas. The chapters are written by experts sharing their experiences very lucidly through case studies, suitable illustrations and tables. The contents have been designed to fulfil the needs of geospatial, data science, agricultural, natural resources and environmental sciences of traditional universities, agricultural universities, technological universities, research institutes and academic colleges worldwide. It will help the planners, policymakers and extension scientists in planning and sustainable management of agriculture and natural resources. The authors believe that with its uniqueness the book is one of the important efforts in the contemporary cyber-physical systems.

## Encyclopedia of Bioinformatics and Computational Biology

Author | : Anonim |

Publsiher | : Elsevier |

Total Pages | : 3284 |

Release | : 2018-08-21 |

ISBN | : 0128114320 |

Category | : Medical |

Language | : EN, FR, DE, ES & NL |

**Encyclopedia of Bioinformatics and Computational Biology Book Excerpt:**

Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. Brings together information from computer science, information technology, mathematics, statistics and biotechnology Written and reviewed by leading experts in the field, providing a unique and authoritative resource Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications Includes interactive images, multimedia tools and crosslinking to further resources and databases

## Game Data Science

Author | : Magy Seif El-Nasr,Truong-Huy D. Nguyen,Alessandro Canossa,Anders Drachen |

Publsiher | : Oxford University Press |

Total Pages | : 414 |

Release | : 2021 |

ISBN | : 019289787X |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Game Data Science Book Excerpt:**

"Game Data Science delivers a thorough introduction to this new domain and serves as a definitive guide to the methods and practices of computer science, analytics, and data science as applied to video games. It is the ideal resource for professional learners and students seeking to understand how data science is used within the game development and production cycle, as well as within the interdisciplinary field of games research." -- back cover.