Machine Learning for Practical Decision Making

A Multidisciplinary Perspective with Applications from Healthcare, Engineering and Business Analytics

AvChristo El Morr,Manar Jammal

Inbunden, Engelska, 2022

Del i serien International Series in Operations Research & Management Science

1 457 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

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.

Produktinformation

Utgivningsdatum:2022-11-30
Mått:155 x 235 x 32 mm
Vikt:887 g
Format:Inbunden
Språk:Engelska
Serie:International Series in Operations Research & Management Science
Antal sidor:465
Förlag:Springer International Publishing AG
ISBN:9783031169892

Utforska kategorier

Mer om författaren

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.

Innehållsförteckning

1. Introduction to Machine Learning.- 2. Statistics.- 3. Overview of Machine Learning Algorithms.- 4. Data Preprocessing.- 5. Data Visualization.- 6. Linear Regression.- 7. Logistic Regression.- 8. Decision Trees.- 9. Naïve Bayes.- 10. K-Nearest Neighbors.- 11. Neural Networks.- 12. K-Means.- 13. Support Vector Machine.- 14. Voting and Bagging.- 15. Boosting and Stacking.- 16. Future Directions and Ethical Considerations.