BM2401 PATTERN RECOGNITION AND NEURAL NETWORKS SYLLABUS | ANNA UNIVERSITY BE BIOMEDICAL ENGINEERING 7TH SEM SYLLABUS REGULATION 2008 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY SEVENTH SEMESTER BE INSTRUMENTATION AND CONTROL ENGINEERING DEPARTMENT SYLLABUS, TEXTBOOKS, REFERENCE BOOKS,EXAM PORTIONS,QUESTION BANK,CLASS NOTES, IMPORTANT 2 MARKS, 8 MARKS, 16 MARKS TOPICS. IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011 2012-2013 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI,TIRUNELVELI,COIMBATORE), 2008 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009
BM2401 PATTERN RECOGNITION AND NEURAL NETWORKS L T P C
3 0 0 3
UNIT I INTRODUCTION AND SIMPLE NEURAL NET 9
Elementary neurophysiology and biological neural network-Artificial neural network –
Architecture, biases and thresholds, Hebb net, Perceptron, Adaline and Madaline.
UNIT II BACK PROPOGATION AND ASSOCIATIVE MEMORY 9
Back propogation network, generalized delta rule, Bidirectional Associative memory,
Hopefield network
UNIT III NEURAL NETWORKS BASED ON COMPETITION 9
Kohonen Self organising map, Learning Vector Quantisation, counter propogation
network.
61
UNIT IV UNSUPERVISED LEARNING AND CLUSTERING ANALYSIS 9
Patterns and features, training and learning in pattern recognition, discriminant functions,
different types of pattern recognition. Unsupervised learning- hierarchical clustering,
partitional clustering. Neural pattern recognition approach – perceptron model
UNIT V SUPERVISED LEARNING USING PARAMETRIC AND NON 9
PARAMETRIC APPROACH
Bayesian classifier, non parametric density estimation, histograms, kernels, window
estimators, k-nearest neighbour classifier , estimation of error rates.
TOTAL : 45 PERIODS
TEXT BOOKS:
1. Hagan, Demuth and Beale, “Neural network design”, Vikas Publishing
House Pvt. Ltd., New Delhi , 2002
2. Freeman J.A., and Skapura B.M, " Neural networks, algorithms, applications and
programming techniques”, Addison – Wesley,2003
3. Duda R.O, Hart P.G, “Pattern classification and scene analysis”, Wiley Edition,2000
4. Earl Gose, Richard Johnsonbaugh, Steve Jost, “Pattern Recognition and Image
Analysis”, Prentice Hall of India Pvt. Ltd., New Delhi, 1999.
REFERENCES:
1. Robert Schalkoff, “ Pattern recognition, Statistical, Structural and neural approaches”
John Wiley and Sons(Asia) Pte. Ltd., Singapore, 2005
2. Laurene Fausett ,” Fundamentals of neural networks – Architectures, algorithms and
applications”, Prentice Hall, 1994.
BM2401 PATTERN RECOGNITION AND NEURAL NETWORKS L T P C
3 0 0 3
UNIT I INTRODUCTION AND SIMPLE NEURAL NET 9
Elementary neurophysiology and biological neural network-Artificial neural network –
Architecture, biases and thresholds, Hebb net, Perceptron, Adaline and Madaline.
UNIT II BACK PROPOGATION AND ASSOCIATIVE MEMORY 9
Back propogation network, generalized delta rule, Bidirectional Associative memory,
Hopefield network
UNIT III NEURAL NETWORKS BASED ON COMPETITION 9
Kohonen Self organising map, Learning Vector Quantisation, counter propogation
network.
61
UNIT IV UNSUPERVISED LEARNING AND CLUSTERING ANALYSIS 9
Patterns and features, training and learning in pattern recognition, discriminant functions,
different types of pattern recognition. Unsupervised learning- hierarchical clustering,
partitional clustering. Neural pattern recognition approach – perceptron model
UNIT V SUPERVISED LEARNING USING PARAMETRIC AND NON 9
PARAMETRIC APPROACH
Bayesian classifier, non parametric density estimation, histograms, kernels, window
estimators, k-nearest neighbour classifier , estimation of error rates.
TOTAL : 45 PERIODS
TEXT BOOKS:
1. Hagan, Demuth and Beale, “Neural network design”, Vikas Publishing
House Pvt. Ltd., New Delhi , 2002
2. Freeman J.A., and Skapura B.M, " Neural networks, algorithms, applications and
programming techniques”, Addison – Wesley,2003
3. Duda R.O, Hart P.G, “Pattern classification and scene analysis”, Wiley Edition,2000
4. Earl Gose, Richard Johnsonbaugh, Steve Jost, “Pattern Recognition and Image
Analysis”, Prentice Hall of India Pvt. Ltd., New Delhi, 1999.
REFERENCES:
1. Robert Schalkoff, “ Pattern recognition, Statistical, Structural and neural approaches”
John Wiley and Sons(Asia) Pte. Ltd., Singapore, 2005
2. Laurene Fausett ,” Fundamentals of neural networks – Architectures, algorithms and
applications”, Prentice Hall, 1994.
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