COS521: Cognitive Knowledge Acquisition (CS, Learning)

This course presents basic frameworks of learning, offering the theoretical underpinning for the development of machine learning algorithms, with an emphasis on the development of naturalistic solutions for the acquisition of symbolically-represented cognitive knowledge. It  examines learning in the limit, the mistake-bounded model of online learning, active learning with queries, and the probably approximately correct model of batch learning. It then discusses learnability in the presence of missing or corrupted information. An effort is made to connect the formal properties of these models to real world situations, and examine the extent to which these properties capture or reflect some aspects of human learning. The relation of learning to the processes of perception and reasoning is also discussed, as well as the relation of learning to other natural processes, including the process of evolution.