Neural Network

Biological and Artificial Neuron, Perceptron model, Adaline model, Different types of Activation functions, Learning Techniques: Supervised and Unsupervised, Multilayered feed forward Networks, Back propagation algorithm and its improvements, Applications of Back propagation algorithm to statistical pattern recognition, classification and regression problems, Advantages of Neural Networks over statistical classification techniques, Performance Surfaces and Optimum Points, Steepest Descent, Stable learning Rates, Minimizing Along a line ,Newton’s method and conjugate method. Recurrent networks, Radial Basis Function Networks as an interpolation model, Time delay neural networks for forecasting problems, Probabilistic Neural Networks, Kohonen’s self organizing map, Self organizing maps with quadratic functions and its applications medical imaging, Adaptive Resonance, Theory model, Applications of Art model for knowledge acquisition, Extensive sessions in MATLAB for solving statistical pattern recognition, classification, regression and prediction problems using different kinds of Neural Network models.