ITEC460 - Introduction to Neural Networks

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The Aim of the Course

This course is an introduction to neural networks with both theoretical and practical issues being considered. Upon completion of this course, the student should understand the main neural network architectures and learning algorithms and be able to apply neural networks to real classification problems. Topics covered include single layer perceptions, multi-layer perceptions, associative memory networks, discrete hopfield networks, radial basis function networks and self-organizing networks.

Lecture Hours

Lec.: Tue. 10.30-12.20, CT 001 & Thu. 12.30-14.20, CTL224, Office Hour: Thu. 10.30-11.20, AT105.

Lecture Notes

1. Introduction (PDF) (PPSX)
2. Single Layer Perceptron (PDF) (PPSX)
3. Multilayer Perceptron (MLP)  (PDF) (PPSX)
4. Associative Memory Neural Network (PDF) (PPSX)
5. Discrete Hopfield Network (PDF) (PPSX)
6. Radial Basis Function (RBF) Networks  (PDF) (PPSX)

Research Papers

1. Anil K. Jain, Jianchang Mao, K.M. Mohiuddin, "Artificial Neural Networks: A Tutorial," Computer, vol. 29, no. 3, pp. 31-44, March, 1996.

Grading System

Projects Quizes Midtetrm Exam Final Exam
20 %15 %25 %40 %