Lecture 7 Artificial neural networks: Supervised learning ■ Introduction, or how the brain works ■ The neuron as a simple computing element ■ The perceptron ■ Multilayer neural networks ■ Accelerated learning in multilayer neural networks ■ The Hopfield network ■ Bidirectional associative memories (BAM) ■ Summary  Negnevitsky, Pearson Education, 2002 1 Introduction, or how the brain works Machine learning involves adaptive mechanisms that enable computers to learn from experience, learn by example and learn by analogy. Learning capabilities can improve the performance of an intelligent system over time. The most popular approaches to machine learning are artificial neural networks and genetic algorithms. This lecture is dedicated to neural networks.  Negnevitsky, Pearson Education, 2002 2 ■ A neural network can be defined as a model of reasoning based on the human brain. The brain consists of a densely interconnected set of nerve cells, or basic information-processing units, called neurons. ■ The human brain incorporates nearly 10 billion neurons and 60 trillion connections, synapses, between them. By using multiple neurons simultaneously, the brain can perform its functions much faster than the fastest computers in existence today.  Negnevitsky, Pearson Education, 2002 3 ■ Each neuron has a very simple structure, but an army of such elements constitutes a tremendous processing power. ■ A neuron consists of a cell body, soma, a number of fibers called dendrites, and a single long fiber called the axon.  Negnevitsky, Pearson Education, 2002 4 Biological neural network Synapse Axon Soma Synapse Dendrites Axon Soma Dendrites Synapse  Negnevitsky, Pearson Education, 2002 5 ■ Our brain can be considered as a highly complex, non-linear and parallel information-processing system. ■ Information is stored and processed in a neural network simultaneously throughout the whole network, rather than at specific locations. In other words, in neural networ

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