EE 258 - Neural Networks

Description
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.
Credits
3
Attributes
Not a Service Learning Course
Recent Professors
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Recent Semesters
Fall 2019, Fall 2018, Fall 2017
Offered
MW, TuTh
Avg. Class Size
80
Avg. Sections
1