This course will cover advanced topics at the intersection of probabilistic modeling, neural computation, and artificial intelligence, with an emphasis on approaches using generative models. Students will develop a deep understanding of how probability theory provides a unified framework for understanding the human brain and intelligence more broadly. Topics include fundamental concepts in probability theory and philosophical foundations of uncertainty, methods for learning and inference in probabilistic models, and probabilistic methods in machine learning like VAEs. The course will cover how probabilistic models have contributed to our understanding of learning, perception, and decision-making. The course will emphasize both theoretical foundations and practical implementations, preparing students to engage with current research in computational neuroscience and AI. Prior exposure to linear algebra, multivariable calculus, machine learning, and probability theory are all recommended.

This course will cover advanced topics at the intersection of probabilistic modeling, neural computation, and artificial intelligence, with an emphasis on approaches using generative models. Students will develop a deep understanding of how probability theory provides a unified framework for understanding the human brain and intelligence more broadly. Topics include fundamental concepts in probability theory and philosophical foundations of uncertainty, methods for learning and inference in probabilistic models, and probabilistic methods in machine learning like VAEs. The course will cover how probabilistic models have contributed to our understanding of learning, perception, and decision-making. The course will emphasize both theoretical foundations and practical implementations, preparing students to engage with current research in computational neuroscience and AI. Prior exposure to linear algebra, multivariable calculus, machine learning, and probability theory are all recommended.

This course is to fulfill the work plan agreed by the student and the dissertation adviser. The guiding principle of the Dissertation Research course is to complete the doctoral dissertation research proposed by the doctoral candidate and approved by the candidate’s dissertation committee. The course consists of carrying out the thesis research, including collection and analysis of data, and completion and public defense of the dissertation document for partial fulfillment of the requirements of the PhD degree in Cognitive Science. This course can only be taken after successful completion of COGS-800 Cognitive Science Qualifying Examination.

Doctoral students in the Ph.D. in Cognitive Science are expected to conduct research under the supervision and guidance of their faculty advisor. After completion of all degree requirements, with the exception of COGS-890 Cog Sci Dissertation and including completion of at least 60 semester credit hours, students may register for COGS-888 Cog Sci Continuation of Dissertation in order to maintain their status in the Ph.D. in Cognitive Science program.

An introduction to the underlying concepts of computer vision and image understanding. The course will consider fundamental topics, including image formation, edge detection, texture analysis, color, segmentation, shape analysis, detection of objects in images and high level image representation. Depending on the interest of the class, more advanced topics will be covered, such as image database retrieval or robotic vision. Programming assignments are an integral part of the course. Note: students who complete CSCI-431 may not take CSCI-631 for credit.