Course Name: 

Artificial Neural Networks (IT358)


B.Tech (IT)


Programme Specific Electives (PSE)

Credits (L-T-P): 

(3-0-2) 4


Introduction to Artificial Neural Networks , Artificial Neuron Model and Linear Regression, Gradient Descent Algorithm, Nonlinear Activation Units and Learning Mechanisms, Learning Mechanisms, Associative Memory Model, Statistical Aspects of Learning, Single-Layer Perceptron, Least Mean Squares Algorithm, Perceptron Convergence Theorem, Bayes Classifier, Back Propagation Algorithm, Multi-Class Classification Using Multi-layered Perceptrons, Radial Basis Function Network, Introduction to Principal Component Analysis and Independent Component Analysis, Introduction to Self Organizing Maps, Applications and Recent Research Trends


Simon Haykin, “Neural networks - A comprehensive foundations”, Pearson, 2004
Laurene Fausett: “Fundamentals of neural networks: architectures, algorithms and applications”, Prentice Hall
James A. Anderson, “An Introduction to Neural Networks”, Prentice Hall of India.
Yegnanarayana: “Artificial Neural Networks”, Prentice Hall of India,2004.


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