Course info

Module Description

After completing this module, students will be expected to be able to:

1. Demonstrate an understanding of the basic concepts and principles of neural computation as an approach to intelligent problem-solving.
2. Describe the commonly used neural network architectures and learning algorithms.
3. Distinguish classes of problems to which neural networks offer solutions superior to other methods.
4. Design a neural network to solve a particular problem.
5. Implement typical neural networks in software for regression and pattern classification.
6. Gain understanding and knowledge of deep neural networks.

Outline Syllabus

Introduction to Artificial Neural Networks

• basic concepts and principles of ANNs
• biological motivations and brief history of ANNs
• neuron models and neural network architectures
• computational power of ANNs in comparison with conventional AI methods
• ANN applications

Basic Learning Rules and Theories

• basic issues in neural network learning
• derivative-based methods such as error gradient descent learning algorithms
• derivative-free methods such as simulated annealing, genetic algorithm, Hebbian learning and competitive learning
• the bias-variance dilemma in learning from data

Feedforward Neural Networks Using Supervised Learning

• feedforward neural network architectures and supervised learning
• perceptron: architecture, error correction learning, limitations
• multilayer perceptron (MLP): architecture, back-propagation learning algorithm 
• radial basis function (RBF) network: architecture, learning algorithm, comparison with MLP

Self-organising Neural Networks Using Unsupervised Learning

• unsupervised learning: learning without a teacher
• adaptive resonance theory (ART) neural network: architecture, learning algorithm
• self-organising map (SOM) neural network: architecture, learning algorithm

Recurrent Neural Networks

• recurrent neural network architectures
• Hopfield neural network: energy function, Hebbian learning, stability analysis

Deep Neural Networks

• concepts and architectures

ANN Applications and Recent Advances

• basic issues and strategies in neural network applications: data collection and preprocessing, classification, regression, prediction, and intelligent control
• recent advances in neural network research and development: support vector machine (SVM), reinforcement learning, neuro-fuzzy networks, etc.