Overview
- Provides real life examples in healthcare and business
- Designed for novice reader with no technical background
- Uses a hands-on approach that allows the reader to acquire a set of practical machine learning skills
Part of the book series: International Series in Operations Research & Management Science (ISOR, volume 334)
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About this book
This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward approach guided by real-world examples in areas such as health informatics, information technology, and business analytics. The book will help readers understand the various key algorithms, major software tools, and their applications. Moreover, through examples from the healthcare and business analytics fields, it demonstrates how and when ML can help them make better decisions in their disciplines.
The book is chiefly intended for undergraduate and graduate students who are taking an introductory course in machine learning. It will also benefit data analysts and anyone interested in learning ML approaches.
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Keywords
Table of contents (16 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
Christo El Morr, PhD is an Associate Professor of Health Informatics at the School of Health Policy and Management, York University, Canada. He is also a Research Scientist at North York General Hospital, Toronto, Canada. His research subscribes to an Equity Informatics perspective; it covers Patient-Centered Virtual Care (e.g., chronic disease management, mental health), Global Health Promotion for equity (e.g., equity health promotion), Human Rights Monitoring (e.g., disability rights, Gender-Based Violence), and Equity AI (e.g., patient readmission, disability advocacy).
Manar Jammal, PhD is an Assistant Professor at the School of Information Technology, York University, Canada. Her work focuses on developing cutting-edge data analytics techniques and innovative machine learning models in the areas of networking, 5G systems, IoT, and cloud computing. Her research interests include machine learning, software engineering and modeling, distributed systems, cloud computing, network function virtualization, 5G systems, IoT, data analytics, high availability, and software-defined networks.
Hossam Ali-Hassan, PhD is an Associate Professor of Information Systems and Chair of International Studies at York University, Glendon campus, Toronto, Canada. Prior to his academic career, he worked for many years as a network specialist and information technology consultant. He currently teaches a variety of courses at York University such as information systems, business analytics, and supply chain management technology. His research interests include business analytics, data literacy, data visualization, experiential learning, social media, social capital, and job performance.
Walid El-Hallak, BSc Hons is a Lead Developer at Ontario Health, Canada. With 16 years of healthcare consulting experience in the public and private sectors, he is specialized in integrating clinical systems using healthcare standards such as HL7, IHE and DICOM. He has implementedcomplex province-wide eHealth projects such as the Diagnostic Imaging Network for Northern and Eastern Ontario. Holding a BSc Hons in computer science specialisation Bioinformatics. He has also developed statistical models for biological motif sequence discovery.
Bibliographic Information
Book Title: Machine Learning for Practical Decision Making
Book Subtitle: A Multidisciplinary Perspective with Applications from Healthcare, Engineering and Business Analytics
Authors: Christo El Morr, Manar Jammal, Hossam Ali-Hassan, Walid EI-Hallak
Series Title: International Series in Operations Research & Management Science
DOI: https://doi.org/10.1007/978-3-031-16990-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-16989-2Published: 30 November 2022
eBook ISBN: 978-3-031-16990-8Published: 29 November 2022
Series ISSN: 0884-8289
Series E-ISSN: 2214-7934
Edition Number: 1
Number of Pages: XVII, 465
Number of Illustrations: 1 b/w illustrations
Topics: Operations Research/Decision Theory, Health Care Management, Health Informatics, Machine Learning, Artificial Intelligence, Computer Science, general