EHB 420E - Artificial Neural Networks

Course Objectives

1. To provide an understanding of artificial neural networks (ANN)
2. To provide an understanding of how ANN are applied to the real world engineering problems.

Course Description

1. Biological neural systems
2. Introduction to artificial neural networks
3. Linear models for regression and classification
4. Supervised, unsupervised, and self-supervised learning
5. ANNs architectures. Perceptron learning rule. Hebbian learning rule
6. Optimization methods. Gradient descent learning rule
7. Single layer neural networks
8. Multi-layered perceptron design. Back propagation algorithm
9. Radial basis function networks
10. Kohonen’s self-organizing maps
11. ANNs applications: Deep learning, engineering applications, etc.