The report provides a comprehensive introduction to active learning, a subfield of machine learning where algorithms can choose which unlabeled instances to query for labels, aiming to achieve high accuracy with fewer labeled instances. Active learning is particularly useful when labels are expensive or time-consuming to obtain. The report covers various scenarios, including membership query synthesis, stream-based selective sampling, and pool-based active learning, and discusses several query strategy frameworks such as uncertainty sampling, query-by-committee, expected model change, variance reduction, estimated error reduction, and density-weighted methods. It also analyzes empirical and theoretical evidence for active learning, reviews problem setting variants, and explores related research areas like semi-supervised learning, reinforcement learning, and active feature acquisition. The report aims to distill the core ideas, methods, and applications in active learning, providing a valuable resource for researchers and practitioners in the field.The report provides a comprehensive introduction to active learning, a subfield of machine learning where algorithms can choose which unlabeled instances to query for labels, aiming to achieve high accuracy with fewer labeled instances. Active learning is particularly useful when labels are expensive or time-consuming to obtain. The report covers various scenarios, including membership query synthesis, stream-based selective sampling, and pool-based active learning, and discusses several query strategy frameworks such as uncertainty sampling, query-by-committee, expected model change, variance reduction, estimated error reduction, and density-weighted methods. It also analyzes empirical and theoretical evidence for active learning, reviews problem setting variants, and explores related research areas like semi-supervised learning, reinforcement learning, and active feature acquisition. The report aims to distill the core ideas, methods, and applications in active learning, providing a valuable resource for researchers and practitioners in the field.