OVERVIEW SYLLABUS HOMEWORK STUDENTS USER GUIDES JOURNALS CONTACT INFO HOME
--SYLLABUS DOWNLOADS PDF: LINK Suggested text: Neural Networks: A Comprehensive Foundation Author: Simon Haykin, publisher: Prentice Hall, year: 1999 --LECTURE OVERVIEW Overview of neural systems and computer modeling Abstract neuron models and simple neural circuits Feed-forward and recurrent lateral inhibition Unsupervised learning and self-organizing maps Covariance learning and Hopfield networks Supervised learning and back-propagation Reinforcement learning and associative conditioning Informational capacity of neural networks Estimation of probabilities by neural networks Recurrent back-propagation and time series learning Sequential decisions and temporal difference learning Predictor-corrector networks and Kalman filters Genetic algorithms and neural networks Review and case studies