Michael D Stiber
Application of biological computing principles to machine problem solving. State of the art in artificial neural networks (ANNs), including vision, motor control, learning, data analysis. Topics include ANN architectures, algorithms: perceptrons, Widrow-Hoff, backpropagation, Hebbian networks. Prerequisite: CSS 343; may not be repeated; recommended: prior exposure to linear algebra, probability, and calculus.
Computing systems have grown more and more powerful, yet with this increasing power has come increasing complexity and decreasing reliability and usability. One possible solution to this problem is to make these systems more like biological computers: nervous systems and brains. Neurocomputing is the study of biological computing principles for application to machines. This course is an introduction to artificial neural networks (ANNs) and brain modeling. Topics covered include basic neuroscience concepts, optimization, heuristic search, dynamics, control, learning, and genetic algorithms and genetic programming. Applications surveyed include vision, motor control, data analysis, and game playing.
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