Information Theoretic Methods In Data Science
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Information Theoretic Methods In Data Science
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Author : Miguel R. D. Rodrigues
language : en
Publisher: Cambridge University Press
Release Date : 2021-04-08
Information Theoretic Methods In Data Science written by Miguel R. D. Rodrigues and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-08 with Computers categories.
The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.
Information Theory And Statistical Learning
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Author : Frank Emmert-Streib
language : en
Publisher: Springer Science & Business Media
Release Date : 2009
Information Theory And Statistical Learning written by Frank Emmert-Streib and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.
Information Theoretic Perspectives On 5g Systems And Beyond
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Author : Ivana Marić
language : en
Publisher:
Release Date : 2022-06-15
Information Theoretic Perspectives On 5g Systems And Beyond written by Ivana Marić and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-15 with Language Arts & Disciplines categories.
Understand key information-theoretic principles that underpin the design of next-generation cellular systems with this invaluable resource. This book is the perfect tool for researchers and graduate students in the field of information theory and wireless communications, as well as for practitioners in the telecommunications industry.
Model Selection And Multimodel Inference
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Author : Kenneth P. Burnham
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-28
Model Selection And Multimodel Inference written by Kenneth P. Burnham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-28 with Mathematics categories.
A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.
Data Science Concepts And Techniques With Applications
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Author : Usman Qamar
language : en
Publisher: Springer Nature
Release Date : 2023-04-02
Data Science Concepts And Techniques With Applications written by Usman Qamar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-02 with Computers categories.
This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.
35 Key Concepts In Information Theory Explained In 7 Minutes Each
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Author : Nietsnie Trebla
language : en
Publisher: Shelf Indulgence
Release Date :
35 Key Concepts In Information Theory Explained In 7 Minutes Each written by Nietsnie Trebla and has been published by Shelf Indulgence this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
35 Key Concepts in Information Theory Explained in 7 Minutes Each In our increasingly interconnected world, understanding the fundamentals of information theory is essential for navigating the complexities of communication, data analysis, and technological advancement. '35 Key Concepts in Information Theory Explained in 7 Minutes Each' serves as an accessible guide designed for both novices and seasoned professionals seeking to grasp the core principles that underpin this vital field. Each chapter distills a fundamental concept of information theory into a concise, digestible format, taking no more than seven minutes to read. This structured approach enables readers to quickly assimilate knowledge and apply it to real-world situations. Chapters Overview: The Concept of Information: Definition and Measurement - Explore how information is defined and quantified. Entropy: The Measure of Uncertainty - Understand the concept of uncertainty and how it relates to information. Shannon's Noisy Channel Coding Theorem - Learn about the foundational theorem that governs communication in the presence of noise. Information Compression: Lossless vs. Lossy - Discover the techniques behind optimizing data storage and transmission. Mutual Information: A Measure of Shared Information - Dive into the quantification of shared data between systems. The Role of Redundancy in Communication Systems - Understand how redundancy can enhance communication reliability. Channel Capacity: Theoretical Limits of Transmission - Examine the upper limits of data transfer rates. Error Correction Codes: Ensuring Reliable Communication - Explore methods for correcting errors in data transmission. Data Transmission vs. Data Storage: A Distinction - Clarify the differences between these two essential aspects of information handling. The Source Coding Theorem: Optimal Data Representation - Learn how to achieve the most efficient data representation. Applications of Information Theory in Cryptography - Investigate how information theory underpins secure communications. Kolmogorov Complexity: Understanding Algorithmic Information - Delve into measuring the complexity of data sets. Universal Sources and the Concept of Randomness - Explore the nature of randomness and its implications for information theory. The Emergence of Quantum Information Theory - Discover the intersection of quantum mechanics and information science. Information Theory in Machine Learning and AI - Understand the foundational role of information theory in developing smart technologies. Applications in Biology: Biological Information and Genomes - Examine how information theory applies to genetics and biological processes. The Role of Information Theory in Networking - Learn how information theory shapes modern networking protocols. The Information Bottleneck Principle - Explore how to balance the trade-off between complexity and accuracy. Cross Entropy and KL Divergence: Measuring Differences - Understand these important metrics for comparing probability distributions. Predictive Coding: The Brain as a Bayesian Machine - Investigate how the brain processes information through predictive mechanisms. The Impact of Information Theory on Telecom Innovations - Learn how the field has transformed telecommunications. Coding Theorems in Modern Satellite Communication - Explore the application of coding theory in satellite technologies. Information Theory and the Second Law of Thermodynamics - Discover the relationship between information and thermodynamic principles. Game Theory and Information: Strategies Under Uncertainty - Analyze how information theory informs strategic decision-making. Influence of Information Theory on Signal Processing - Learn about the significant impact on how signals are analyzed and processed. Data Privacy and Information Theoretic Security - Examine principles that safeguard data privacy in an interconnected world. Information Theory in Digital Forensics - Understand how these concepts apply to forensic investigations. Adaptive Coding and Modulation Techniques - Explore modern methods for optimizing data transmission. Information Theoretic Essentials in Social Networks - Learn how information flows in social media environments. The Role of Information Theory in Data Science - Delve into the importance of information metrics in data analysis. Causal Inference and the Information Perspective - Explore the use of information theory in determining causality. Information Theory and Structured Prediction - Investigate how structured predictions can be enhanced by information theory. The Future of Information Theory: Challenges and Directions - Discuss emerging challenges and future research directions in the field. Philosophical Implications of Information as a Concept - Reflect on the deeper meanings and impacts of information in society and thought. This book acts as a perfect springboard for those looking to deepen their understanding of information theory, whether for academic purposes, professional development, or personal curiosity. Dive into the world of information with confidence and clarity!
Towards An Information Theory Of Complex Networks
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Author : Matthias Dehmer
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-08-26
Towards An Information Theory Of Complex Networks written by Matthias Dehmer and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-26 with Mathematics categories.
For over a decade, complex networks have steadily grown as an important tool across a broad array of academic disciplines, with applications ranging from physics to social media. A tightly organized collection of carefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities. The book's major goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks. This volume is the first to present a self-contained, comprehensive overview of information-theoretic models of complex networks with an emphasis on applications. As such, it marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks for all scientific disciplines and can serve as a valuable resource for a diverse audience of advanced students and professional scientists. While it is primarily intended as a reference for research, the book could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.
Machine Learning For Engineers
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Author : Osvaldo Simeone
language : en
Publisher: Cambridge University Press
Release Date : 2022-11-03
Machine Learning For Engineers written by Osvaldo Simeone and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-03 with Computers categories.
This self-contained introduction contains all students need to start applying machine learning principles to real-world engineering problems.
Methodologies And Applications Of Computational Statistics For Machine Intelligence
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Author : Samanta, Debabrata
language : en
Publisher: IGI Global
Release Date : 2021-06-25
Methodologies And Applications Of Computational Statistics For Machine Intelligence written by Samanta, Debabrata and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-25 with Computers categories.
With the field of computational statistics growing rapidly, there is a need for capturing the advances and assessing their impact. Advances in simulation and graphical analysis also add to the pace of the statistical analytics field. Computational statistics play a key role in financial applications, particularly risk management and derivative pricing, biological applications including bioinformatics and computational biology, and computer network security applications that touch the lives of people. With high impacting areas such as these, it becomes important to dig deeper into the subject and explore the key areas and their progress in the recent past. Methodologies and Applications of Computational Statistics for Machine Intelligence serves as a guide to the applications of new advances in computational statistics. This text holds an accumulation of the thoughts of multiple experts together, keeping the focus on core computational statistics that apply to all domains. Covering topics including artificial intelligence, deep learning, and trend analysis, this book is an ideal resource for statisticians, computer scientists, mathematicians, lecturers, tutors, researchers, academic and corporate libraries, practitioners, professionals, students, and academicians.
Issues In Information Science Informatics 2011 Edition
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Author :
language : en
Publisher: ScholarlyEditions
Release Date : 2012-01-09
Issues In Information Science Informatics 2011 Edition written by and has been published by ScholarlyEditions this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-09 with Computers categories.
Issues in Information Science: Informatics / 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Information Science—Informatics. The editors have built Issues in Information Science: Informatics: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Information Science—Informatics in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Information Science: Informatics / 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.