| Course Name | Statistical Pattern Recognition |
| Course Code | 26MAT345 |
| Program | 5 Year Integrated M.Sc in Data Science |
| Credits | 3 |
| Campus | Coimbatore |
Introduction to Pattern Recognition, Introduction Pattern recognition systems the design cycle learning and adaptation Applications of Pattern recognition statistical decision theory Examples on pattern recognition, and image processing and analysis.
Parametric methods of Classification, Introduction Bayes theorem and Bayesian decision making classifications based on single and multiple features conditionally independent features discrete and continuous features – Gaussian case and general theory – discriminant functions, decision boundaries and decision regions problems of dimensionality components analysis and discriminants Minimum error rate classification Receiver Operating characteristic curves Estimation of the composition of populations based on machine learning classification.
Nonparametric methods Classification, Introduction – histograms density estimation mixture densities – kernel and window estimators nearest neighbor classification techniques rules and distance metrics – single and k-nearest neighbor techniques adaptive decision boundaries and discriminant functions minimum squared error discriminant functions.
Nonmetric methods of classification, Introduction to Nonmetric methods decision trees and decision regions CART methods algorithm-independent machine learning lack of inherent superiority of any classifier bias and variance for regression and classification resampling or estimating statistics jackknife and bootstrap resampling methods
Methods of Clustering, Introduction to unsupervised learning and clustering data description and clustering criterion functions for clustering hierarchical clustering single linkage algorithm, complete linkage algorithm, average linkage algorithm and Wards algorithm partitional clustering Forgys algorithm – and k-means algorithm
Text Books
References
| Course Outcomes | |
| CO1 | To understand Bayesian decision theory and its use |
| CO2 | To gain knowledge about Bayesian estimation methods |
| CO3 | To apply nonparametric techniques and linear discriminant functions |
| CO4 | To gain knowledge about nonmetric methods and algorithm independent machine learning |
| CO5 | To apply unsupervised learning and clustering |
CO-PO Mapping
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | |
| CO1 | 3 | 2 | 2 | 3 | 2 | 2 | 2 | |||||
| CO2 | 3 | 3 | 3 | 2 | 2 | 2 | ||||||
| CO3 | 3 | 3 | 3 | 2 | 2 | 2 | ||||||
| CO4 | 3 | 3 | 3 | 2 | 2 | 2 | ||||||
| CO5 | 3 | 3 | 3 | 2 | 2 | 2 |
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