Survival analysis is a mature field with decades of methodological development, yet machine learning survival analysis is still taking shape as a discipline in its own right. While machine learning methods for time-to-event prediction are increasingly used in health care, clinical research, actuarial science, engineering, and industry, a critical gap remains in the literature: few texts bridge the theoretical foundations of survival analysis with the methods, workflows, and evaluation tools of modern machine learning. Machine Learning in Survival Analysis fills this gap by providing a systematic treatment of machine learning approaches to time-to-event prediction.