Course Name: 

Artificial Neural Networks (IT804)


M.Tech (IT)


Elective Courses (Ele)

Credits (L-T-P): 

(3-0-0) 3


Introduction to Artificial Neural Networks , Artificial Neuron Model and Linear Regression, Gradient Descent Algorithm, Nonlinear Activation Units and Learning Mechanisms,
Associative Memory Model, Statistical Aspects of Learning, Single-Layer Perceptions, Least Mean Squares Algorithm, Perceptron Convergence Theorem, Bayes Classifier, Back
Propagation Algorithm, Multi-Class Classification Using Multi- layered Perceptrons, Radial Basis Function Network, Principal Component Analysis and Independent Component Analysis, 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.
J A. Freeman, D M. Skapure: Neural Networks Algorithms, Applications & Programming Techniques, Addison-Wesley.
James A. Anderson, “An Introduction to Neural Networks”, Prentice Hall of India.
Yegnanarayana: “Artificial Neural Networks”, Prentice Hall of India, 2004.


Information Technology

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