CSE6701

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Neural Networks

3 Credit Hour Course

Intended For Level 0 Term 0 Students

Prerequisite:

Fundamentals of neural networks; Backpropagation and related training algorithms, Autograd, activation functions, loss functions, regularizations; Supervised Hebbian learning; Cohonen-Grossberg learning; The BAM and the Hopfield memory; Performance surfaces and optimum points, performance optimization, Widrow-Hoff learning; Different types of neural networks: counter propagation, probabilistic, radial basis functions, generalized regression, etc.; Adaptive resonance theory; Dynamical systems and neural control; Associative learning, Competitive networks; The Boltzmann machine; Self-organizing maps; Spatiotemporal pattern classification, The neurocognition; Practical aspects of neural networks.