School of Computer Science & Software Eng., The University of Western Australia

NEURAL COMPUTATION (CITS4210)

• Introduction
• Neurobiology and Architecture
• Treshold Gates
• Supervised Learning in a Single Unit Setting
• Energy Minimization for a Single Unit Supervised Learning - Part A
• Energy Minimization for a Single Unit Supervised Learning - Part B
• Feedforward Multilayer Neural Networks - Backpropagation Algorithm
• Neural Computation - Some Examples
• Discrete Hopfield Networks - Part A
• Discrete Hopfield Networks - Part B
• Separability and Minimal Polynomial Treshold Gate Realization
• General Position and Function Counting Theorem
• Computational Capabilities of Artificial Neural Networks
• Neural Computation and Global Search Techniques

Auxilliary Lecture Notes

• First lecture note - Introduction
• Second lecture note - Neurobiology and Architectures of ANNs
• Neural Network Learning Rules.
• Single Layer Perceptron classifiers
• Multi-Layer Perceptron and Backpropagation
• Associative Memories and Discrete Hopfield Network
• A review in one slide
• Neural Networks based on Competition
• Neural Networks for Constrained Optimization

Some useful links

• Neural Networks: FAQs