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Enables readers to start doing actual data analysis fast for a truly hands-on learning experience This concise and very easy-to-use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences. The book places great emphasis on both data analysis and drawing conclusions from empirical observations. It also provides formulas where needed in many places, while always remaining focused on concepts rather than mathematical abstraction. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics offers a variety of popular statistical analyses and data management tasks using SPSS that readers can immediately apply as needed for their own research, and emphasizes many helpful computational tools used in the discovery of empirical patterns. The book begins with a review of essential statistical principles before introducing readers to SPSS. The book then goes on to offer ch

SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics

﻿Researchers from social sciences and economics consider trust a requirement for successful cooperation between people. It helps to judge the risk in situations, in which a person has the choice to rely on another one. In the future, technical systems will face similar situations. Assume, for example, self-organised robots, which reload some goods at a large logistics centre together. For this, they will need a mechanism like trust. This book gives the reader tools to understand trust and to introduce a trust mechnism into own applications. The tools include generic requirements for own trust mechanisms and the Enfident Model - a conceptual, implementation-independent model of trust. These theoretical tools are complemented with state-of-the-art algorithms from statistical relational learning. Finally, as an example, all this is applied to cooperating cognitive vehicles. As trust is a social phenomenon, this evaluation features a virtual society of vehicles, which cooperate in a vehicu

Trust between Cooperating Technical Systems

This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods.  For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark.    This book contains three parts.  The first part explores the fundamental tools of data science. It includes basic graph the

Mathematical Problems in Data Science

Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms.  Many of the mathematical concepts are explained in an intuitive manner.  The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with.     Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a comp

An Introduction to Machine Learning

This edited volume on data science features a variety of research ranging from theoretical to applied and computational topics. Aiming to establish the important connection between mathematics and data science, this book addresses cutting edge problems in predictive modeling, multi-scale representation and feature selection, statistical and topological learning, and related areas.  Contributions study topics such as the hubness phenomenon in high-dimensional spaces, the use of a heuristic framework for testing the multi-manifold hypothesis for high-dimensional data, the investigation of interdisciplinary approaches to multi-dimensional obstructive sleep apnea patient data, and the inference of a dyadic measure and its simplicial geometry from binary feature data.    Based on the first Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place in 2017 at the Institute for Compuational and Experimental Research in Mathematics (ICERM) in Providence, Rhod

Research in Data Science

This volume comprises papers dedicated to data science and the extraction of knowledge from many types of data: structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering and pattern recognition methods; strategies for modeling complex data and mining large data sets; applications of advanced methods in specific domains of practice. The contributions offer interesting applications to various disciplines such as psychology, biology, medical and health sciences; economics, marketing, banking and finance; engineering; geography and geology; archeology, sociology, educational sciences, linguistics and musicology; library science. The book contains the selected and peer-reviewed papers presented during the European Conference on Data Analysis (ECDA 2013) which was jointly held by the German Classification Society (GfKl) and the French-speaking Classification Society (SFC) in July 2013 at the University of Luxembourg.

Data Science, Learning by Latent Structures, and Knowledge Discovery

ENbook.pl

pbAn Introduction to Statistical Learningb 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, marketing, and 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. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. pp ppFour of the authors co-wrote iAn Introduction to Statistical Learning, With Applications in R iISLR, which has become a mainstay of

An Introduction to Statistical Learning | James Gareth | Twarda

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. Written in readable and concise style and devoted to key learning problems, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

The Nature of Statistical Learning Theory | Vapnik Vladimir | Twarda | Twarda

Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.

Phase Transitions in Machine Learning | Saitta Lorenza | Twarda | Twarda

pThis book is aimed to compile typical fundamental to advanced statistical methods to be used for health data sciences. This book promotes the applications to health and health-related data. However, the models in this book can be used to analyse any kind of data. The data are analysed with the commonly used statistical software of RSAS with online supplementary on SPSSStata. The data and computing programs will be available to facilitate readers' learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models, compilations of a variety of publicly availab

Statistical Analytics for Health Data Science with SAS and R | Wilson Jeffrey | Twarda | Twarda

This book surveys a broad range of topics in probability and mathematical statistics. It provides the statistical background that a computer scientist needs to work in the area of machine learning.

All of Statistics | Wasserman Larry | Twarda

Provides a thorough grounding in basic mathematical and statistical techniques, helping students to learn how to make decisions when presented with incomplete information. This book features coverage of statistical methods, management mathematics and probability and extensive examples and questions.

Business Mathematics and Statistics | Mousley Ben | Corvinus University of Budapest | Twarda

pThis book constitutes the refereed proceedings of the 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, held in Portoroz, Slovenia, in June12-15, 2023.pThe 23 full papers and 21 short papers presented together with 3 demonstration papers were selected from 108 submissions. The papers are grouped in topical sections on machine learning and deep learning explainability and transfer learning natural language processing image analysis and signal analysis data analysis and statistical models knowledge representation and decision support.pppp

Artificial Intelligence in Medicine | Juarez Jose M. | Twarda

The book introduces the most important statistical methods for analyzing multivariate data sets. After reading the book, the reader female, male is able to autonomously carry out comprehensive statistical analyzes of data. The steps for finding a solution with SPSS are shown in the book. The individual procedures are explained using easy-to-understand examples to ensure learning success. Previous knowledge of statistics is helpful for reading the book, but not essential. The procedures presented are plausible even without prior basic knowledge of statistics. This book was written over a period of twenty semesters and benefited from the questions raised by the listeners in my lecture.

Analysis of Multivariate Data with SPSS | Arrenberg Jutta | Twarda

pThis textbook presents the fundamental concepts and theories in thermal physics and elementary statistical mechanics in a very simple, systematic and comprehensive way. This book is written in a way that it presents the topics in a holistic manner with end-of-chapter exercises and examples where concepts are supported by numerous solved examples and multiple-choice questions to aid self-learning. The textbook also contains illustrated diagrams for better understanding of the concepts. The book will benefit students who are taking introductory courses in thermal physics, thermodynamics and statistical mechanics.ppp

Thermal and Statistical Physics | Sharma Sandeep | Twarda

pemMachine Learning Toolbox for Social Scientistsem covers predictive methods with complementary statistical tools that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in econometrics textbooks nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at 

Machine Learning Toolbox for Social Scientists | Aydede Yigit | Twarda

This book provides a concise survey and description of recent collision detection methods for robot manipulators. Beginning with a review of robot kinodynamic models and preliminaries on basic statistical learning methods, the book covers fundamental aspects of the collision detection problem, from collision types and collision detection performance criteria to model-free versus model-based methods, and the more recent data-driven learning-based approaches to collision detection. Special effort has been given to describing and evaluating existing methods with a unified set of notation, systematically categorizing these methods according to a basic set of criteria, and summarizing the advantages and disadvantages of each method. This book is the first to comprehensively organize the growing body of learning-based collision detection methods, ranging from basic supervised learning methods to more advanced approaches based on unsupervised learning and transfer learning techniques. Step-by

Collision Detection for Robot Manipulators | Park Kyu Min | Twarda

pThe book is a collection of high-quality peer-reviewed research papers presented in the First International Conference on MAchine inTelligence for Research  Innovations MAiTRI 2023 Summit, held at Dr B R Ambedkar National Institute of Technology Jalandhar, Panjab, India during 1 - 3 September 2023. This book focuses on recent advancement in the theory and realization of machine intelligence MI and their tools and growing applications such as machine learning, deep learning, quantum machine learning, real-time computer vision, pattern recognition, natural language processing, statistical modelling, autonomous vehicles, human interfaces, computational intelligence, and robotics.p

Machine Intelligence for Research and Innovations | Verma Om Prakash | Twarda

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for co

Evaluating Learning Algorithms | Japkowicz Nathalie | Twarda

pThis book is an introduction text to distress risk and corporate failure modelling techniques. It illustrates how to apply a wide range of corporate bankruptcy prediction models and, in turn, highlights their strengths and limitations under different circumstances. It also conceptualises the role and function of different classifiers in terms of a trade-off between model flexibility and interpretability.p pJones's illustrations and applications are based on actual company failure data and samples. Its practical and lucid presentation of basic concepts covers various statistical learning approaches, including machine learning, which has come into prominence in recent years. The material covered will help readers better understand a broad range of statistical learning models, ranging from relatively simple techniques, such as linear discriminant analysis, to state-of-the-art machine learning methods, such as gradient boosting machines, adaptive boosting, random forests, and deep learnin

Distress Risk and Corporate Failure Modelling | Jones Stewart | Twarda

This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling. These features mean that they can be applied to market-risk problems to overcome classic problems associated with statistical models. br

Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk | Mostafa Fahed | Paperback | Twarda

pThis book presents a variety of advanced statistical methods at a level suitable for advanced undergraduate and graduate students as well as for others interested in familiarizing themselves with these important subjects. It proceeds to illustrate these methods in the context of real-life applications in a variety of areas such as genetics, medicine, and environmental problems.p pThe book begins in Part I by outlining various data types and by indicating how these are normally represented graphically and subsequently analyzed. In Part II, the basic tools in probability and statistics are introduced with special reference to symbolic data analysis. The most useful and relevant results pertinent to this book are retained. In Part III, the focus is on the tools of machine learning whereas in Part IV the computational aspects of BIG DATA are presented.p pThis book would serve as a handy desk reference for statistical methods at the undergraduate and graduate level as well as be useful in 

Statistical Inference and Machine Learning for Big Data | Alvo Mayer | Twarda | Twarda

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pHow can music withstand the death and destruction brought on by war Global conflicts of the 20th century fundamentally transformed not only national boundaries, power relations, and global economies, but also the arts and culture of every nation involved. An important, unacknowledged aspect of these conflicts is that they have unique musical soundtracks. iMusic in World War IIi explores how music and sound took on radically different dimensions in the United States and Europe before, during, and after World War II. Additionally, the collection examines the impact of radio and film as the disseminators of the war's musical soundtrack. Contributors contend that the European and American soundtrack of World War II was largely one of escapism rather than the lofty, solemn, heroic, and celebratory mode of war music in the past. Furthermore, they explore the variety of experiences of populations forced from their homes and interned in civilian and POW camps in Europe and the United States, 

Music in World War II | Potter Pamela M. | Twarda

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statistical learning book