Welcome to the PatternTech Suite project, developed as part of the course Statistical Modeling and Pattern Recognition. This repository showcases various advanced pattern recognition algorithms and methodologies, organized into two main sections:
- Principal Component Analysis (PCA): A technique for dimensionality reduction and feature extraction.
- Linear Discriminant Analysis (LDA): A method used for classification and dimensionality reduction.
- Bayes Classification: An approach based on Bayes' theorem for probabilistic classification.
- Perceptron: A foundational neural network model used for binary classification.
- Convolutional Neural Networks (CNNs): Advanced neural networks designed for image and spatial data processing.
- Maximum Likelihood Estimation (MLE): A method for estimating model parameters.
- K-Means Clustering: An algorithm for partitioning data into clusters.
The detailed project report is available in both Greek and English:
I would like to express my gratitude to my colleague and friend, John Sinanis, for his valuable contributions and insightful feedback on the reports.