IT358

Course Name: 

Artificial Neural Networks(IT358)

Programme: 

B.Tech (IT)

Category: 

Programme Specific Electives (PSE)

Credits (L-T-P): 

(3-0-2) 4

Content: 

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

References: 

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.

Department: 

Information Technology
 

Contact us

G. Ram Mohana Reddy

Professor and Head,
Department of Information Technology, NITK, Surathkal,
P. O. Srinivasnagar, Mangalore - 575 025
Karnataka, India.
Ph.:    +91-824-2474056
Email:  infotech[AT]nitk[DOT]ac[DOT]in
            infotech[AT]nitk[DOT]edu[DOT]in

Sowmya Kamath S (Web Admin)

Connect with us

We're on Social Networks. Follow us & stay in touch.