Principles of Machine Learning

This chapter presents a comprehensive overview of machine learning, covering supervised learning techniques such as linear regression and decision trees, with applications in medical image analysis. Unsupervised learning is explored, emphasizing clustering algorithms such as k-means and hierarchical clustering for pattern identification in medical imaging datasets. The chapter introduces reinforcement learning in medical imaging, tackling applications such as optimizing radiation dose in CT scans. It outlines critical machine learning components, including data acquisition, model architecture, loss functions, and evaluation metrics. These metrics encompass classification, receiver operating characteristic (ROC) analysis, precision-recall curves, and metrics such as intersection over union (IoU) and dice coefficient. The  chapter enriches the reader’s understanding of intricate aspects in assessing machine learning models.