An introduction to computational theories of human cognition. Emphasizes questions of inductive learning and inference, and the representation of knowledge. Project required for graduate credit. This class is suitable for intermediate to advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.
Investigates fundamental issues in photography, both analog and digital, and the nature of the photographic image as well as nontraditional ways of exploring the photographic vision. Explores relationship of image to language as well as the issues of meaning, interpretation, and their relationship to culture.
Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning. Expressions of degree of belief were used in language long before people began codifying the laws of probability theory. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning. An introduction to the use of probability theory to capture aspects of cognitive processes. Emphasizes history of probability theory and computational approaches to probabilistic and causal inference. This class is suitable for advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.
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