Prerequisites: Basic Calculus and Linear Algebra, Signals and Systems.
This course and its sequel, EECE 651(Computational Intelligence) together comprise the series of the Soft Computing courses. It covers the neural network architecture, its learning algorithms, and its applications to pattern recognition, robotics, and control. The architecture consists of a great variety of paradigms including the Multilayer Perceptron along with Back Propagation learning, Support Vector Machines, Kohonen’s Clustering Network and the Associative Memory Network.