FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title: Information Theory for Data Science
 Author(s) Changho Suh
 Publisher: Now Publishers (April 3, 2023); eBook (Creative Commons Licensed)
 License(s): Creative Commons License (CC)
 Hardcover/Paperback: 250 pages
 eBook: PDF
 Language: English
 ISBN10/ASIN: 1638281149
 ISBN13: 9781638281146
 Share This:
Book Description
This book aims at demonstrating modern roles of Information Theory in a widening array of data science applications, focuses on applications that arise in data science, including social networks, ranking, and machine learning.
About the Authors Changho Suh is an Associate Professor of Electrical Engineering at KAIST and an Associate Head of KAIST AI Institute.

Information Theory, Inference and Learning Algorithms
This textbook introduces Information Theory in tandem with applications, alongside practical communication systems, such as arithmetic coding for data compression and sparsegraph codes for errorcorrection.

The Functional Analysis of Quantum Information Theory
This book provides readers with a concise introduction to current studies on operatoralgebras and their generalizations, operator spaces and operator systems, with a special focus on their application in Quantum Information Science.

Introduction to Data Science (Rafael A. Irizarry)
Introduces concepts and skills that can help tackling realworld data analysis challenges. Covers concepts from probability, statistical inference, linear regression, and machine learning. Helps developing skills such as R programming, data wrangling, etc.

Computational and Inferential: The Foundations of Data Science
Step by step, you'll learn how to leverage algorithmic thinking and the power of code, gain intuition about the power and limitations of current machine learning methods, and effectively apply them to real business problems.

Data Science: Theories, Models, Algorithms, and Analytics
It provides a bucket full of information regarding Data Science, covers a wide variety of sections by giving access to theories, data science algorithms, tools and analytics. You'll explore the right approach to best practices to guide you along the way.

An Introduction to Machine Learning Interpretability
Understanding and trusting models and their results is a hallmark of good science. Get an applied perspective on how this applies to machine learning, including fairness, accountability, transparency, and explainable AI.

Hyperparameter Tuning for Machine Learning: A Practical Guide
This open access book provides a wealth of handson examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.

Approaching (Almost) Any Machine Learning Problem
This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.

An Introduction to Quantum Machine Learning for Engineers
This book provides a selfcontained introduction to Quantum Machine Learning for an audience of engineers with a background in probability and linear algebra, describes the necessary background, concepts, and tools, covers parametrized quantum circuits, etc.

Mathematics for Machine Learning (Marc P. Deisenroth, et al.)
This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It provides a beautiful exposition of the mathematics underpinning modern machine learning.

Metalearning: Applications to Automated Machine Learning
This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and Automated Machine Learning (AutoML). It can help developers to develop systems that can improve themselves through experience.

Convex Optimization for Machine Learning (Changho Suh)
This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal is to help develop a sense of what convex optimization is, and how it can be used.

Pen and Paper Exercises in Machine Learning (Michael Gutmann)
This is a collection of (mostly) penandpaper exercises in machine learning. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning.

Interpretable Machine Learning: Black Box Models Explainable
This book explains to you how to make (supervised) machine learning models interpretable. The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and NLP tasks.

Machine Learning with Neural Networks (Bernhard Mehlig)
This modern and selfcontained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. It provides comprehensive coverage of neural networks, their evolution, their structure, their applications, etc.
:






















