Intel Corporation recently awarded a 6-months grant in the amount of $25,074 to Prof. Shayok Chakraborty. The objective of this project, titled “Active Learning for Computer Vision”, is to develop a semester-long curriculum on the emerging topic of active learning for computer vision applications. The rapid escalation of technology and the widespread emergence of modern technological equipment have resulted in the generation of large amounts of digital data (in the form of images, videos and text) in the modern era. This has expanded the possibilities of solving real-world problems using computational learning frameworks. However, annotating the data with class labels, to induce a machine learning model, is an expensive process in terms of time, labor and human expertise. This has set the stage for research in the field of active learning. Active learning algorithms automatically identify the salient and exemplar samples from large amounts of unlabeled data. This tremendously reduces human annotation effort, as only a few samples, which are identified by the algorithm, need to be labeled manually. Further, since the model gets trained on the most informative samples from the data population, it has better generalization capability than a standard passive learner. This project will produce a comprehensive survey of the existing work on active learning for vision applications, which can be used to educate students and next generation machine learning researchers in industry and academia on this important research challenge.