Information Theory for Data Science

• Main Author: Suh, Changho (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: Now Publishers 2023
• Series: NowOpen
• Subjects: Information technology: general issues; Information Theory, Data Science, Source Coding, Channel Coding, DNA sequencing, DNA sequencing, Top-K ranking, Supervised learning, Unsupervised Learning, Generative Adversarial Networks (GANs), TensorFlow
• Online Access: DOAB: download the publication; DOAB: description of the publication

Summary

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.

Physical Description

1 electronic resource (417 p.)

ISBN

9781638281153
9781638281146

Access

Open Access