CIS 565 - Introduction to Artificial Neural Networks

3 credit(s) Upon sufficient interest

Perceptrons and the Perceptron Convergence Theorem; non-linear optimization, gradient descent methods; neural net architecture, conjugate-gradient and recurrent networks; Hopfield networks, Kohonen’s feature maps; non-neural clustering algorithms.