Advanced Artificial Neural Networks

“Classification of computing techniques, basic neural network models, linear and non-linear separable problems, feed forward neural network models, feedback neural network models, single and multi-layer neural networks, learning strategies in computers, supervised and unsupervised neural network learning algorithms. Hebb net, Adaline and Madaline, back-propagation and variants, radial basis function networks, discrete and continuous Hopfield networks, counter-propagation learning algorithms, self-organizing maps, learning vector quantization, adaptive resonance theory, Boltzmann, Gaussian and Cauchy machines, neo-cognition and recent trends in neural networks.
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Hours: XYZ where X = Lecture, Y = Lab, Z = Credit
All hours are per week.
3 Lab hours constitute 1 credit hour
1 credit hour implies 1 lecture of 50mins per academic week. 16 weeks in total.
Pre-Requisite courses are courses required to be completed before this course may be taken
Co-Requisite courses are courses required to be taken along with this course