Neural Networks
Spring 2016
Credits: 3
Level: Graduate, Optional
Prerequisite:
Hours: Friday, 7.45-14
Location: 302
Course Syllabus:
1-Introduction
2-Neuron Model and Network Architectures
3- Perceptron, Hamming Network and Hopfield Network
4-Perceptron Learning Rule
5-Supervised Hebbian Learning
6-Performance Surfaces
7-Performance Optimization
8-Widrow-Hoff Learning
9-Backpropagation
10-Variations on Backpropagation
Grading:
1. Homework and Projects: 50%
2. Mid-term exam: 25% (if any)
3. Final exam: 25%-50%
References:
1- Neural Network Design, Martin T. Hagan, Howard B. Demuth, Mark H. Beale, ISBN: 0-9717321-0-8
2-Neural Networks and Learning Machines, Simon S. Haykin, Prentice Hall, 2009
Power Points
Ch1 Ch2 Ch3
Ch4 Ch7 Ch8
Ch9 Ch10 Ch11
Ch12 MATLAB