Fundamentals and applications of neural networks and learning processes. Course covers models of a neuron, perceptrons, Linear Mean Square (LMS) algorithm, multilayer perceptrons, back propagation algorithm, and radial basis function networks. Deep feedforward networks, regularization for deep learning, and optimization for deep models. Convolutional neural networks. Recurrent and recursive networks. Prerequisite: Graduate standing or instructor consent. Allowed Declared Major: Electrical Engineering. Enrollment Requirements: Prerequisite: Allowed Declared Major: Electrical Engineering.