Artificial Neural Networks Detailed Syllabus for B.Tech third year second sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.
The detailed syllabus for Artificial Neural Networks B.Tech 2016-2017 (R16) third year second sem is as follows.
B.Tech. III Year II Sem. L/T/P/C
Course Code:MT622OE 3/0/0/3
Course Objectives:
- To understand the biological neural network and to model equivalent neuron models.
- To understand the architecture, learning algorithm and issues of various feed forward and feedback neural networks.
Course Outcomes: By completing this course the student will be able to:
- Create different neural networks of various architectures both feed forward and feed backward.
- Perform the training of neural networks using various learning rules.
- Perform the testing of neural networks and do the perform analysis of these networks for various pattern recognition applications.
UNIT – I Introduction: A Neural Network, Human Brain, Models of a Neuron, Neural Networks viewed as Directed Graphs, Network Architectures, Knowledge Representation, Artificial Intelligence and Neural Networks Learning Process: Error Correction Learning, Memory Based Learning, Hebbian Learning, Competitive, Boltzmann Learning, Credit Assignment Problem, Memory, Adaption, Statistical Nature of the Learning Process
UNIT – II Single Layer Perceptron: Adaptive Filtering Problem, Unconstrained Organization Techniques, Linear Least Square Filters, Least Mean Square Algorithm, Learning Curves, Learning Rate Annealing Techniques, Perceptron –Convergence Theorem, Relation Between Perceptron and Bayes Classifier for a Gaussian Environment Multilayer Perceptron: Back Propagation Algorithm XOR Problem, Heuristics, Output Representation and Decision Rule, Computer Experiment, Feature Detection
UNIT – III Back Propagation: Back Propagation and Differentiation, Hessian Matrix, Generalization, Cross Validation, Network Pruning Techniques, Virtues, and Limitations of Back Propagation Learning, Accelerated Convergence, Supervised Learning
TEXT BOOKS:
- Neural Networks a Comprehensive Foundations, Simon Haykin, PHI edition.
REFERENCE BOOKS:
- Artificial Neural Networks – B. Yegnanarayana Prentice Hall of India P Ltd 2005
- Neural Networks in Computer Inteligance, Li Min Fu TMH 2003
- Neural Networks -James A Freeman David M S Kapura Pearson Education 2004.
- Introduction to Artificial Neural Systems Jacek M. Zurada, JAICO Publishing House Ed. 2006.
For all other B.Tech 3rd Year 2nd Sem syllabus go to JNTUH B.Tech Mechanical Engineering (Mechatronics) 3rd Year 2nd Sem Course Structure for (R16) Batch.
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