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. Prerequisites include CSS 342 and prior exposure to linear algebra, probability, and calculus.

There is a supplementary reading for probability, as well as one for compression.

Remember to check our assignments page periodically for homework clarifications.

Our final will be on December 16th, at the usual class time and location.