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Course Description:

Course Objectives:

Course Contents:

Laboratory works: Practical should be focused on Single Layer Perceptron, Multilayer Perceptron, Supervised Learning, Unsupervised Learning, Recurrent Neural Network, Linear Prediction and Pattern Classification

Text Book:

1. Simon Haykin, Neural Networks and Learning Machines, 3rd Edition, Pearson

Reference Books:

1. Christopher M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 2003
2. Martin T. Hagan, Neural Network Design, 2nd Edition PWS pub co.

Unit 1: Introduction to Neural Network (4 Hrs.)

Unit 2: Rosenblatt’s Perceptron (3 Hrs.)

Unit 3: Model Building through Regression (5 Hrs.)