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.
K. Ming Leung
Office hours :
"Neural Network Design", by Martin T. Hagan, Howard B. Demuth, Mark H. Beale, ISBN-10: 0-9717321-0-8.
Grading System (Tentative):
Attendance/Participation (5%), HW (35%), Midterm-Exam (30%), Project (30%).
Lecture Notes :
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.