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oapen-20.500.12657-871652024-03-28T14:03:04Z Information Theory for Data Science Suh, Changho Information Theory, Data Science, Source Coding, Channel Coding, DNA sequencing, DNA sequencing, Top-K ranking, Supervised learning, Unsupervised Learning, Generative Adversarial Networks (GANs), TensorFlow thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics Information theory deals with mathematical laws that govern the flow, representation and transmission of information. The most significant achievement of the field is the invention of digital communication which forms the basis of our daily-life digital products such as smart phones, laptops and any IoT devices. Recently it has also found important roles in a spotlight field that has been revolutionized during the past decades: data science. This book aims at demonstrating modern roles of information theory in a widening array of data science applications. The first and second parts of the book covers the core concepts of information theory: basic concepts on several key notions; and celebrated source and channel coding theorems which concern the fundamental limits of communication. The last part focuses on applications that arise in data science, including social networks, ranking, and machine learning. The book is written as a text for senior undergraduate and graduate students working on Information Theory and Communications, and it should also prove to be a valuable reference for professionals and engineers from these fields. 2024-01-24T13:03:49Z 2024-01-24T13:03:49Z 2023 book 9781638281146 https://library.oapen.org/handle/20.500.12657/87165 eng NowOpen application/pdf Attribution-NonCommercial 4.0 International 9781638281153.pdf Now Publishers 10.1561/9781638281153 10.1561/9781638281153 13f74cc9-b7cd-4eb0-8f5f-900c90afdd9f 9781638281146 417 open access
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Information theory deals with mathematical laws that govern the flow, representation and transmission of information. The most significant achievement of the field is the invention of digital communication which forms the basis of our daily-life digital products such as smart phones, laptops and any IoT devices. Recently it has also found important roles in a spotlight field that has been revolutionized during the past decades: data science.
This book aims at demonstrating modern roles of information theory in a widening array of data science applications. The first and second parts of the book covers the core concepts of information theory: basic concepts on several key notions; and celebrated source and channel coding theorems which concern the fundamental limits of communication. The last part focuses on applications that arise in data science, including social networks, ranking, and machine learning.
The book is written as a text for senior undergraduate and graduate students working on Information Theory and Communications, and it should also prove to be a valuable reference for professionals and engineers from these fields.
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