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You are here:Open notes-->VTU-->PATTERN-RECOGNITION-10CS664-

PATTERN RECOGNITION 10CS664

How to study this subject



Subject Code: 10CS664 I.A. Marks : 25
Hours/Week : 04 Exam Hours: 03
Total Hours : 52 Exam Marks: 100
PART – A
UNIT – 1 6 Hours
Introduction: Machine perception, an example; Pattern Recognition System;
The Design Cycle; Learning and Adaptation.
UNIT – 2 7 Hours
Bayesian Decision Theory: Introduction, Bayesian Decision Theory;
Continuous Features, Minimum error rate, classification, classifiers,
discriminant functions, and decision surfaces; The normal density;
Discriminant functions for the normal density.
UNIT – 3 7 Hours
Maximum-likelihood and Bayesian Parameter Estimation: Introduction;
Maximum-likelihood estimation; Bayesian Estimation; Bayesian parameter
estimation: Gaussian Case, general theory; Hidden Markov Models.
UNIT – 4 6 Hours
Non-parametric Techniques: Introduction; Density Estimation; Parzen
windows; kn – Nearest- Neighbor Estimation; The Nearest- Neighbor Rule;
Metrics and Nearest-Neighbor Classification.
PART – B
UNIT – 5 7 Hours
Linear Discriminant Functions: Introduction; Linear Discriminant
Functions and Decision Surfaces; Generalized Linear Discriminant
Functions; The Two-Category Linearly Separable case; Minimizing the
Perception Criterion Functions; Relaxation Procedures; Non-separable
Behavior; Minimum Squared-Error procedures; The Ho-Kashyap procedures.
UNIT – 6 6 Hours
Stochastic Methods: Introduction; Stochastic Search; Boltzmann Learning;
Boltzmann Networks and Graphical Models; Evolutionary Methods.
UNIT – 7 6 Hours
Non-Metric Methods: Introduction; Decision Trees; CART; Other Tree
Methods; Recognition with Strings; Grammatical Methods. 61
UNIT – 8 7 Hours
Unsupervised Learning and Clustering: Introduction; Mixture Densities
and Identifiability; Maximum-Likelihood Estimates; Application to Normal
Mixtures; Unsupervised Bayesian Learning; Data Description and Clustering;
Criterion Functions for Clustering.
Text Books:
1. Richard O. Duda, Peter E. Hart, and David G.Stork: Pattern
Classification, 2nd Edition, Wiley-Interscience, 2001.
Reference Books:
1. Earl Gose, Richard Johnsonbaugh, Steve Jost: Pattern Recognition
and Image Analysis, PHI, Indian Reprint 2008. 



Official Notes


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Notes from other sources

http://pesitsouth.pes.edu/pdf/CE/RS/PR_QB.pdf

http://pesitsouth.pes.edu/pdf/CE/new/10CS664_.pdf

http://pesitsouth.pes.edu/pdf/CE/new/10CS664.pdf

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