The field of machine learning has rapidly transformed the landscape of modern computational sciences, revolutionizing disciplines ranging from medical imaging to personalized medicine. This book is an endeavor to provide a rigorous yet accessible exposition of machine learning principles, methodologies, and applications, with a particular emphasis on their integration into biomedical and clinical research. By bridging theoretical foundations with real-world implementations, this work aspires to serve as both an academic reference and a practical guide for researchers, students, and professionals navigating the complexities of machine learning in precision medicine.

This book would not have been possible without the contributions and support of several individuals whose dedication and expertise greatly enhanced its quality. I would like to express my sincere gratitude to Dr. Lan Ma, Assistant Director of the Biocomputational Engineering Program and Acting Principal Investigator of the project, after I left the University of Maryland, whose guidance and leadership have been instrumental in shaping the direction of this work and the Biocomputational Engineering team. Her unwavering support and insightful contributions were invaluable throughout this process.

I am also deeply appreciative of Eileen G. Harrington, Assistant Director & Health and Life Sciences Librarian at Priddy Library, University of Maryland Libraries, The Universities at Shady Grove. Her expertise and support in facilitating access to critical resources significantly contributed to the research and development of this work.

A special acknowledgment is extended to Harmen Siezen, whose meticulous efforts in preparing the chapters and refining the mathematical content significantly strengthened the clarity and depth of this book. His expertise in mathematical formulations and rigorous revision played a crucial role in ensuring the precision and coherence of the presented concepts.

I am also grateful to Nicole Vigil for her diligent work on the initial LaTeX content, which provided the structural foundation for this manuscript. Additionally, I would like to recognize Christine Vassell and Arielle Scott for their contributions in drafting the first chapter and integrating essential coding components, which greatly enriched the practical aspects of this book.

To all who have contributed their time, expertise, and dedication to this work, I extend my heartfelt thanks. I hope this book serves as a valuable resource for those seeking to explore, understand, and advance the field of machine learning in biomedical research and beyond.

Bard Yousefi Rodd
Biocomputational Engineering, University of Maryland College Park
Summer 2024