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Neural Networks
OVERVIEW
CEA CAPA Partner Institution: Goldsmiths, University of London
Location: London, England
Primary Subject Area: Computer Sciences
Instruction in: English
Course Code: IS53002S
Transcript Source: TBD
Course Details: Level 400
Recommended Semester Credits: 4
DESCRIPTION
The module introduces the theory and practice of neural computation. It offers the principles of neuro-computing with artificial neural networks widely used for addressing real-world problems such as classification, regression, pattern recognition, data mining, time series prediction, etc.
Two main topics are covered: supervised and unsupervised learning:
Supervised learning is studied using linear perceptrons, and non-linear models such as probabilistic neural networks, multilayer perceptrons, and radial-basis function networks.
Unsupervised learning is studied using Kohonen networks. Recurrent networks of the Hopfield type are briefly covered. There are contemporary training techniques offered for all these neural networks.
Program implementations in Matlab of the studied neural networks are provided.
Two main topics are covered: supervised and unsupervised learning:
Supervised learning is studied using linear perceptrons, and non-linear models such as probabilistic neural networks, multilayer perceptrons, and radial-basis function networks.
Unsupervised learning is studied using Kohonen networks. Recurrent networks of the Hopfield type are briefly covered. There are contemporary training techniques offered for all these neural networks.
Program implementations in Matlab of the studied neural networks are provided.
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