Biological neural systems. Introduction to artificial neural networks. Supervised and unsupervised learning. Merits and limitations of neurocomputing. Perceptron as a linear classifier. Perceptron learning algorithms: ADALINE, MADALINE. Multi-layer perceptron. Generalized delta-rule. Backpropagation learning. Linear associated memory networks. Bidirectional memory. Recurrent networks. Hopfield networks. Stochastic neural networks. Boltzmann machine. Simulated annealing. Kohonen networks. Self-organizing feature maps. Adaptive resonance theory: ART1 architecture. Hybrid networks. Radial basis function networks. Counterpropagation networks. Fuzzy neural networks. Genetic algorithms. Hardware implementation of neural networks. Application of neural networks.
| AUs | 4.0 AUs |
| Grade Type | |
| Prerequisite | |
| Not Available To Programme | BCE(2011-onwards), BCG(2011-onwards), CE(2011-onwards), CSC(2011-onwards) |
| Not Available To All Programme With | |
| Not Available As BDE/UE To Programme | |
| Not Available As Core To Programme | |
| Not Available As PE To Programme | |
| Mutually Exclusive With | CSC422, SC436 |
| Not Offered As BDE | Yes |
| Not Offered As Unrestricted Elective | |
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