This report provides a general review of the active learning literature. Active learning is a subfield of machine learning where the learning algorithm can achieve greater accuracy with fewer labeled training instances by selecting the most informative data. Active learning is well-motivated in many modern machine learning problems where unlabeled data may be abundant but labels are difficult, time-consuming, or expensive to obtain. The report discusses various active learning scenarios, query strategies, and related research areas. It includes an overview of the empirical and theoretical evidence for active learning, problem setting variants, and related topics in machine learning research. The report also provides an introduction to active learning, examples, and further reading. It covers different active learning scenarios such as membership query synthesis, stream-based selective sampling, and pool-based active learning. It also discusses various query strategy frameworks such as uncertainty sampling, query-by-committee, expected model change, variance reduction, Fisher information ratio, and estimated error reduction. The report also discusses density-weighted methods and their advantages over other query strategies. The analysis of active learning includes empirical and theoretical evidence, and the report concludes with a discussion of the applications and challenges of active learning in various machine learning tasks.This report provides a general review of the active learning literature. Active learning is a subfield of machine learning where the learning algorithm can achieve greater accuracy with fewer labeled training instances by selecting the most informative data. Active learning is well-motivated in many modern machine learning problems where unlabeled data may be abundant but labels are difficult, time-consuming, or expensive to obtain. The report discusses various active learning scenarios, query strategies, and related research areas. It includes an overview of the empirical and theoretical evidence for active learning, problem setting variants, and related topics in machine learning research. The report also provides an introduction to active learning, examples, and further reading. It covers different active learning scenarios such as membership query synthesis, stream-based selective sampling, and pool-based active learning. It also discusses various query strategy frameworks such as uncertainty sampling, query-by-committee, expected model change, variance reduction, Fisher information ratio, and estimated error reduction. The report also discusses density-weighted methods and their advantages over other query strategies. The analysis of active learning includes empirical and theoretical evidence, and the report concludes with a discussion of the applications and challenges of active learning in various machine learning tasks.