6EX6.1-NEURAL NETWORKS |
Units: I-INTRODUCTION: |
Introduction to Neural Networks, Biological basis for NN,
Human brain, Models of a Neuron, Directed Graphs, Feedback, Network architectures,
Knowledge representation, Artificial intelligence & Neural Networks.
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Units: II-LEARNING PROCESSES: |
Introduction, Error -Correction learning, Memory
-based learning, Hebbian learning, Competitive learning, Boltzmann learning, Learning
with a Teacher & without a teacher, learning tasks, Memory, Adaptation. |
Units: III-SINGLE LAYER PERCEPTRONS: |
Introduction, Least-mean-square
algorithm, Learning Curves, Learning rate Annealing Techniques, Perceptron, Perceptron
Convergence Theorem.
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Units: IV-MULTI LAYER PERCEPTRONS: |
Introduction, Back-Propagation
Algorithm, XOR Problem, Output representation and Decision rule, Feature Detection,
Back-Propagation and Differentiation, Hessian Matrix, Generalization.
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Units: V-RADIAL-BASIS FUNCTION NETWORKS & SELF-ORGANISING
MAPS: |
Introduction to Radial basis function networks, Cover’s Theorem on the
Separability of Patterns, Interpolation Problem, Generalized Radial-Basis function
networks, XOR Problem. Self-Organizing map, Summary of SOM Algorithm, Properties of
the feature map.
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