JNTUK B.Tech Pattern Recognition (Elective – III) gives you detail information of Pattern Recognition (Elective – III) R13 syllabus It will be help full to understand you complete curriculum of the year.
Course Objectives
The course is designed to introduce students to theoretical concepts and practical
issues associated with pattern recognition
Course Outcomes
Design systems and algorithms for pattern recognition (signal classification), with focus on sequences of patterns that are analyzed using, e.g., hidden Markov models (HMM),
- Analyse classification problems probabilistically and estimate classifier performance,
- Understand and analyse methods for automatic training of classification systems,
- Apply Maximum-likelihood parameter estimation in relatively complex probabilistic models, such as mixture density models and hidden Markov models,
- Understand the principles of Bayesian parameter estimation and apply them in relatively simple probabilistic models
Syllabus
UNIT-I: Introduction: Machine perception, pattern recognition example, pattern recognition systems, the Design cycle, learning and adaptation
Bayesian Decision Theory: Introduction, continuous features – two categories classifications, minimum error-rate classification-zero–one loss function, classifiers, discriminant functions, and decision surfaces.
UNIT-II: Normal density: Univariate and multivariate density, discriminant functions for the normal Density different cases, Bayes decision theory – discrete features, compound Bayesian decision theory and context
UNIT-III : Maximum likelihood and Bayesian parameter estimation: Introduction, maximum likelihood Estimation, Bayesian estimation, Bayesian parameter estimation–Gaussian case
UNIT-IV : Un-supervised learning and clustering: Introduction, mixture densities and identifiability, maximum likelihood estimates, application to normal mixtures, K-means clustering. Date description and clustering – similarity measures, criteria function for clustering
UNIT-V : Pattern recognition using discrete hidden Markov models: Discrete-time Markov process, Extensions to hidden Markov models, three basic problems of HMMs, types of HMMs
UNIT-VI : Continuous hidden Markov models : Continuous observation densities, multiple mixtures per state, speech recognition applications.
Text Books
- Pattern classifications, Richard O. Duda, Peter E. Hart, David G. Stroke. Wiley student edition, Second Edition.
- Pattern Recognition, An Introduction, V Susheela Devi, M Narsimha Murthy, Universiy Press
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