| Course Name | Pattern Recognition |
| Course Code | 24ASD641 |
| Program | M.Sc. in Applied Statistics and Data Analytics |
| Credits | 3 |
| Campus | Coimbatore , Kochi |
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 Ward’s algorithm – partitional clustering – Forgy’s algorithm – and k-means algorithm.
Course Outcomes:
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CO01: |
To get an idea about pattern recognition with suitable examples |
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CO02: |
To gain knowledge about parametric classification methods using Bayesian decision making approach |
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CO03: |
To apply nonparametric techniques such as nearest neighbor, adaptive discriminant functions and decision regions based on minimum squared error |
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CO04: |
To gain knowledge about nonmetric methods, classification trees and some resampling methods CO-PO Mapping:
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CO05: |
To apply study and apply various clustering methods |
Text Books/References Books:
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