This course gives an
introduction to neural network models and their applications. Organization and
learning in neural network models including perceptrons,
adalines, backpropagation networks,
recurrent networks, adaptive resonance theory and the neocognitron
are discussed. Application in areas such as decision systems, nonlinear control,
speech processing and vision is explored.
The pre-requisite for this course
is CS 540 or its equivalent. Undergraduate student may take this course if they
have already taken CS 1114,
and the undergraduate math sequence.
Instructor :
K. Ming Leung
E-mail :
Office :
LC 127
Office hours :
Textbook :
"Neural Network Design",
by Martin T. Hagan, Howard B. Demuth, Mark H. Beale, ISBN-10: 0-9717321-0-8.
Classroom :
RH 317
Grading System (Tentative):
Attendance/Participation (5%), HW
(35%), Midterm-Exam (30%), Project (30%).
Lecture Notes :
Introduction:
Introduction to Artificial Neuron
Networks.
Neural Networks for Pattern
Classification.
Perceptron for Pattern Classification.
ADALINE for Pattern Classification.
Backpropagation for
Multilayer Neural Network
Neural Networks for Pattern Association
Iterative Bidirectional Memory
Iterative Discrete Hopfield Net
Assignments :
HW01 (Due February 10)
HW02 (Due March 3)
HW05 (Due April 14)
HW06 (Due May 5)
HW07 (Due April 28)
Midterm: (open book midterm) April 7.