CS6759 Course Outline
Artificial Neural Networks
This course introduces and relates the basic concept of
neural networks. The course will provide a current and coherent view
of artificial neural networks. The neural net
algorithms will be discussed to understand contemporary neurocomputing
It emphasizes mathematical analysis of neural networks,
methods for training networks, and application of networks to practical
problems. Neural network implementation will be discussed to understand
contemporary neurocomputing and soft-computing techniques.
Students in computer science, engineering, and behavioral science can obtain
immediate benefits to visualize new approaches and interdisciplinary
Course Note: see /usr/local/pub/cs/cs6759
J. Freeman and D. Skapura, ``Neural Networks: Algorithm,
Applications, and Programming Techniques'',
``Adaptive Pattern Recognition and Neural Networks''
R. Schalkoff ``Pattern Recognition: Statistical Structural and Neural
Approaches'', John Wiley, 1992,
M. Hagan, H. Demuth, and M. Beale
``Neural Network Design'', PWS, 1996.
L. Fausett ``Fundamentals of Neural Networks: Architectures, Algorithm,
Prentice Hall, 1994.
J. Principe, N. Euliano, and C. Lefebvre ``Neural and Adaptive Systems''
John Wiley and Sons, Inc, 1999.
Research Survey and Presentation 10%
Term Project /Presentation 30%
Final Exam 30%
4 labs & assignments.
Must complete term project & presentation
a) Course information
b) Introduction to Neuro c) perceptron
Generalized Delta Rule
a)Linear Associators (BAM) b) Matrix Associative Memory
a) Discrete Hopfield Networks,
b) Continuous Hopfield Networks, c) Traveling-Salesperson Problem
Instar, outstar, Learning Vector Quantalization (LVQ, LVQ2, LVQ3)
Self-Organize Map(Kohonon SOM)
Adaptive Resonance Theory (ART1, ART2)
Recurrent Neural networks
Speach recognition by Spatiotemporal Neural networks
a) Boltzmann Machine, b) Counterpropagation Networks