Hemachandran K - Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

First edition published 2022

by CRC Press

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2022 selection and editorial matter, Hemachandran K, Shubham Tayal, Preetha Mary George, Praveen Singla and Utku Kose; individual chapters, the contributors

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Library of Congress Cataloging-in-Publication Data

Names: K., Hemachandran, editor. | Tayal, Shubham, editor. | George, Preetha Mary, editor. | Singla, Parveen, editor. | Kose, Utku, 1985- editor.

Title: Bayesian reasoning and Gaussian processes for machine learning applications / edited by Hemachandran K., Shubham Tayal, Preetha Mary George, Parveen Singla, Utku Kose.

Description: First edition. | Boca Raton : Chapman & Hall/CRC Press, 2022. | Includes bibliographical references and index. | Summary: The book Bayesian Reasoning and Gaussian Processes for Machine Learning Applications talks about Bayesian Reasoning and Gaussian Processes in machine learning applications. Bayesian methods are applied in many areas such as game development, decision making and drug discovery. It is very effective for machine learning algorithms for handling missing data and for extracting information from small datasets. This book introduces a statistical background which is needed to understand continuous distributions and it gives an understanding on how learning can be viewed from a probabilistic framework. The chapters of the book progress into machine learning topics such as Belief Network, Bayesian Reinforcement Learning etc., which is followed by Gaussian Process Introduction, Classification, Regression, Covariance and Performance Analysis of GP with other models. This book is aimed primarily at graduates, researchers and professionals in the field of data science and machine learningProvided by publisher.

Identifiers: LCCN 2021052540 (print) | LCCN 2021052541 (ebook) | ISBN 9780367758479 (hardback) | ISBN 9780367758493 (paperback) | ISBN 9781003164265 (ebook)

Subjects: LCSH: Bayesian statistical decision theoryData processing. | Gaussian processesData processing. | Machine learning.

Classification: LCC QA279.5 .B43 2022 (print) | LCC QA279.5 (ebook) | DDC 006.3/101519542dc23/eng/20220128

LC record available at https://lccn.loc.gov/2021052540

LC ebook record available at https://lccn.loc.gov/2021052541

ISBN: 978-0-367-75847-9 (hbk)

ISBN: 978-0-367-75849-3 (pbk)

ISBN: 978-1-003-16426-5 (ebk)

DOI: 10.1201/9781003164265

Typeset in Palatino

by codeMantra

When we look into the past years, we can see an explosion in the applications of machine learning, particularly in e-commerce, social media, gaming, drug discovery, and many other verticals. These applications were focused on predictive accuracy and involved huge amounts of data. Bayesian methods give superpowers to machine learning algorithms, in handling missing data and in extracting information from small data sets. Bayesian methods help estimate uncertainty in predictions, which enhances the field of medicine. They allow to compress models a hundredfold and to automatically tune hyperparameters by saving time and money. In Bayesian Reasoning and Gaussian Processes for Machine Learning Applications, we discuss the basics of Bayesian methods, define probabilistic models, and make predictions using them. We discuss the automated workflow and some advanced techniques on how to speed up the process. We also look into the applications of Bayesian methods in deep learning and to generate images.

This book is designed to encourage researchers and students from multiple disciplines toward the arena of applications of machine learning. It aims to introduce a statistical background needed to understand continuous distributions and how learning can be viewed from a probabilistic framework. It also discusses machine learning topics such as belief network, Bayesian reinforcement learning, Gaussian process with classification, regression, covariance, and performance analysis of Gaussian processes with other models. This book is segmented into ten chapters.

gives a panoramic view on Bayesian Network interface on diabetes risk prediction data. The book gives an insight into how new drugs can cure severe diseases with Bayesian methods.

We hope our attempt in this book will be beneficial for the student community, industrialists, researchers, their mentors, and to all people who wish to explore the applications of machine learning. We are greatly thankful to our contributors who hail from renowned institutes and industries that made a remarkable contribution by imparting their knowledge for the welfare of society. We express our sincere, wholehearted thanks to our editorial and production teams for their relentless contribution and for rendering unconditional support to publish this book on time.

Hemachandran K has been a passionate teacher for 14years, with 5years of research experience. He is a strong educational professional with a flair for science, highly skilled in artificial intelligence and machine learning. After earning a PhD in embedded systems at Dr. M.G.R. Educational and Research Institute, India, he started conducting interdisciplinary research in artificial intelligence. He is an open-minded and positive person who has stupendous peer-reviewed publication records with more than 20 journals and international conference publications. He served as an effective resource person at various national and international scientific conferences. He has a rich research experience in mentoring undergraduate and postgraduate projects. He holds two patents to his credentials. He has life membership in esteemed professional institutions. He was a pioneer in establish Single Board Computer lab at Ashoka Institutions, Hyderabad, India. Because of his self-paced learning schedule and thirst for upgrading and updating learning skills, he was awarded around 15 online certificate courses conferred by COURSERA and other online platforms. His editorial skills led him to be included as an editorial board member for numerous reputed SCOPUS/SCI journals.