This book focuses on practical applications of machine learning models in biomedical engineering with "hands-on" tutorials using Python and existing libraries such as Scikit-Learn and PyTorch. It provides an overview of a broad range of machine learning models such as decision trees, random forests, regression, neural networks, principal component analysis and clustering with examples drawn from machine learning applications on biomedical datasets (e.g., clinical data, physiological signals and medical images) and diseases (e.g., cancer and cardiovascular disease). The book also covers the basic approaches for model selection and performance assessment (e.g., use of cross-validation and receiver operating characteristic curves) as well as strengths and limitations of each machine learning algorithm. This book serves as a textbook for an introductory machine learning class, where educators can easily incorporate machine learning into the biomedical engineering curriculum. Supplemental materials such as lecture slides, codes and exercises are provided.

Patjanaporn "Sang" Chalacheva, PhD, is an Associate Teaching Professor of Biomedical Engineering and Associate Department Head for Education at Carnegie Mellon University. Dr. Chalacheva teaches computational courses in both undergraduate and graduate courses such as Introduction to Machine Learning for Biomedical Engineers, Projects in Biomedical AI, Biostatistics, and Biomedical Systems Modeling and Analysis. Her research involves computational modeling applied to sickle cell disease and sleep disorders, as well as integrating AI/ML into engineering education.

Michael Khoo, PhD, is Dean's Professor of Biomedical Engineering, with secondary appointment in Pediatrics, at the University of Southern California. He was Department Chair of Biomedical Engineering and Co-Director of Education and Outreach in the NSF Engineering Research Center on Biomimetic Microelectronic Systems at USC. He is a Fellow of the IEEE, Biomedical Engineering Society, the American Institute of Medical and Biological Engineering, and the International Academy of Medical and Biological Engineering. His expertise includes biomedical modeling and physiological control systems, with applications in sleep disorders, sickle cell anemia, and metabolic syndrome.