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Course Criteria
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3.00 Credits
An introduction to the study of the brain and behavior from the point of view of cognitive science, including approaches from psychology, philosophy, neuropsychology, and computational modeling. Includes phenomena involving sensory systems, memory, decision making, language, and communication. (3-0) Y
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3.00 Credits
Basic frames of reference in 20th-century psychology and their historical development in Western thought since 1600 with an emphasis on issues involved with minds, brains, and machines. Includes behaviorism, learning theory, artificial intelligence, gestalt, structural and cognitive approaches. Prerequisite: PSY 2301 or CGS 2301. (Same as PSY 3360) (3-0) Y
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3.00 Credits
Laboratory experience in designing and conducting empirical investigations in cognitive science, with a major emphasis on writing research reports. (This course fulfills the advanced writing requirement for Cognitive Science majors and 3 hours of the Communication component of the Core Curriculum). Prerequisite: PSY 3392 or PSY 3490. (Same as PSY 3393( (3-0) R
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3.00 Credits
Auto-associative, associative, competitive learning, recurrent, and back-propagation artificial neural network algorithms in a "hands-on" micro-computer laboratoryenvironment using special simulation software. Applications to perceptual, cognitive, computational, and neuroscience problems. Linear Algebra (MATH 2419) and Computer Programming experience are recommended but not required. (3-0) T
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3.00 Credits
Theory and research on perception, learning, thinking, psycholinguistics, and memory. (Offered in the spring semester.) Prerequisite: PSY 2301 or CGS 2301. (Same as PSY 3361) (3-0) Y
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3.00 Credits
Probabilistic-based methods for natural language understanding using the MATLAB programming language. (3-0) T
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3.00 Credits
Vector calculus and vector calculus-based probability theory with artificial neural network modeling applications. Intended to provide mathematics preparation for CGS 4314 and CGS 4315. Prerequisites: Either (1) Linear algebra, multivariable calculus, STAT 4351 or EE 3341, CGS 3342, or (2) consent of instructor. (3-0) T
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3.00 Credits
Mathematical tools for investigating the asymptotic behavior of both deterministic and stochastic nonlinear dynamical systems for the purposes of building computational models in the fields of neuroscience, psychology, and artificial intelligence. Topics include: artificial neural networkarchitectures, Lyapunov stability theory, nonlinear optimization theory, stochastic approximation theory, and the Gibbs Sampler. Prerequisite: CGS 4313 or equivalent or consent of instructor. (Same as CS 4314) (3-0) T
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3.00 Credits
Mathematical tools for the design and evaluation of artificially intelligent deterministic and stochastic nonlinear dynamical systems for the purposes of building computational models in the fields of neuroscience, psychology, and artificial intelligence. Topics include: (1) Markov Random Field probability representations, and (2) asymptotic mathematical statistical theory for: parameter estimation, model selection, and hypothesis testing. Prerequisite: CGS 4314 or consent of instructor. (Same as CS 4315) (3-0) T
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3.00 Credits
Methods and principles of human-computer interaction (HCI), user-centered design (UCD), and usability evaluation. Provides broad overview of HCI and how HCI informs UCD processes throughout product development lifecycle. (Same as CS 4352) (3-0) T
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