Artificial Neural Networks

Artificial Neural Networks
Hours: 3 0 3

Neural network basics, Hebb net, Perceptron, Adaline and Madaline, Hetero-associative and Auto-associative Networks, Discrete Hopfield Network, Bi-directional Associative Memory (BAM), Backpropagation Neural Network (BPN), Varients of BPN, Simulations Using Backpropagation, Radial Basis Function Networks, Neural Nets Based on Competition, Self-Organization Maps (SOMs), Learning Vector Quantization (LVQ), Counterpropagation Betworks, Adaptive Resonance Theory (ART), Bolzmann Machine, Continuous Hopfield Network, Gaussian and Cauchy Machines, Neo-cognition, Spatiotemporal Pattern classifiers, Neurodynamics & Neurodynamic Programming, Dynamically Driven Recurrent Networks, Temporal Processing using Feedforward Nets, Genetic Algorithms, Case Studies

Pre-requisites: CS351
Co-requisites: none

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

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