Pattern Recognition Course Project with Multiple Algorithm Implementations

In my pattern recognition course project, I implemented several fundamental algorithms including Bayes classification, Fisher linear discriminant analysis, PCA and LDA for feature extraction, K-means clustering, and Parzen window estimation. These algorithms provide comprehensive capabilities for data classification, useful feature extraction, and effective data clustering. The implementation features detailed code comments throughout each module, making the programs easy to understand and modify. Key functions include probabilistic modeling for Bayes classification, covariance matrix operations for Fisher discriminant, eigenvalue decomposition for PCA/LDA dimensionality reduction, centroid-based clustering for K-means, and kernel density estimation for Parzen windows. I have also included test datasets that allow users to validate the accuracy and reliability of these algorithms. The project demonstrates practical implementation of core pattern recognition techniques with emphasis on code clarity and reproducibility.