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  • 9:01 PM, Saturday, 16 Oct 2021


Course Postgraduate
Semester Electives
Subject Code AVD867
Subject Title Pattern Recognition and Machine Learning

Syllabus

PR overview ‐ Feature extraction ‐ Statistical Pattern Recognition ‐ Supervised Learning ‐ Parametric methods ‐ Non parametric methods; ML estimation ‐ Bayes estimation ‐ k NN approaches.Dimensionality reduction, data normalization. Regression, and time series analysis. Linear discriminat functions. Fishers linear discriminant and linear perceptron. Kernel methods and Support vector machine. Decision trees for classification. Unsupervised learning and clustering. K ‐ means and hierarchical clustering. Decision Trees for classification. Ensemble/ Adaboost classifier, Soft computing paradigms for classification and clustering. Applications to document analysis and recognition

Text Books

Same as Reference

References

1. Pattern classification, Duda and Hart, John Wiley and sons ,2001.

2. Machine learning, T M Mitchel, McGraw Hills 1997 Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 2006.