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TEL 613E - Statis.Pattern .Analy.&Classif

Course Objectives

1. To introduce the fundamentals of statistical pattern analysis and classification with examples from several application areas.
2. Techniques for handling multidimensional data of various types and scales along with algorithms for clustering and classifying data will be explained.

Course Description

Fundamentals of statistical pattern recognition. Feature extraction, classification. Multidimensional probability distributions. Bayesian estimation, linear discriminant functions, density estimation, supervised and unsupervised classification. Multidimensional maximum likelihood and MAP estimation. Eigenvalue decomposition, PCA. Parzen windows, LMS and SVM classifiers. K-nearest neighbor classification. K-means clustering. Mixture models, expectation maximization. Bayesian learning. Feature subset selection. Combination of classifiers , bagging, boosting and sensor fusion. Performance estimation, ROC, f-measure, recall, precision, confidence interval. Hidden Markov models

Course Coordinator
Bilge Günsel Kalyoncu
Course Language
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