mca Syllabus
Pattern Recognition
Code: CS 801F
Contact: 3L
Credits: 3
Allotted Hrs: 39L
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Topic
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Syllabus
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1. Introduction (4L)
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Examples; The nature of statistical pattern recognition; Three learning paradigms; The sub-problems of pattern recognition; The basic structure of a pattern recognition system; Comparing classifiers. |
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2. Bayes Decision Theory (7L)
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General framework; Optimal decisions; Classification; Simple performance bounds. |
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3. Learning - Parametric Approaches (4L)
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Basic statistical issues; Sources of classification error; Bias and variance; Three approaches to classification: density estimation, regression and discriminant analysis; Empirical error criteria; Optimization methods; Failure of MLE; |
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4. Parametric Discriminant Functions (4L)
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Linear and quadratic discriminants; Shrinkage; Logistic classification; Generalized linear classifiers; Perceptrons; Maximum Margin; Error Correcting Codes; |
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5. Error Assessment (4L)
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Sample error and true error; Error rate estimation; Confidence intervals; Resampling methods; Regularization; Model selection; Minimum description length; Comparing classifiers |
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6. Nonparametric Classification (4L)
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Histograms rules; Nearest neighbor methods; Kernel approaches; Local polynomial fitting; Flexible metrics; Automatic kernels methods |
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7. Feature Extraction (6L)
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Optimal features; Optimal linear transformations; Linear and nonlinear principal components; Feature subset selection; Feature Extraction and classification stages, Unsupervised learning and clustering, Syntactic pattern recognition, Fuzzy set Theoretic approach to PR, |
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8. Margins and Kernel Based Algorithms (3L)
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TAdvanced algorithms based on the notions of margins and kernelsopic
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9. Applications of PR (3L)
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Speech and speaker recognition, Character recognition, Scene analysis. |
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