Pattern Recognition

Statistical classification, dimensionality reduction, and neural network implementations.

The Pattern Recognition repository serves as a comprehensive collection of algorithms and techniques implemented from scratch and using standard libraries. It covers the spectrum from statistical decision theory to modern neural network architectures.

Rather than just listing the code, this project visualizes the decision boundaries, feature spaces, and optimization landscapes that are central to understanding pattern recognition.

🚀 View Full Project & Code on GitHub

Key Results & Visualization

Here are key visualization results obtained from different models implemented in this repository.

1. Linear Regression

Closed Form Solution:
Closed Form Linear Regression

Stochastic Gradient Descent (SGD):
SGD Result

2. Classification Models

Binary Classification (Logistic Regression)

Logistic Regression Test Set

Bayesian Classification Boundaries

Bayesian Plot 2 Bayesian Plot 3

Multiclass Classification Strategies (Softmax)

OVA Strategy OVO Strategy Softmax Result

Naïve Bayes Classification Results

Classification Report Confusion Matrix

Quadratic Multiclass Classification

3. Clustering & Compression

K-Means Image Compression Comparison

Original Image (Gray) Compressed Image (K-Means)

Technical Overview

This project explores various methodologies in pattern recognition:

  • Statistical Classifiers: Implementation of Bayes Rule and Maximum Likelihood Estimation.
  • Non-Parametric Methods: K-Nearest Neighbors (KNN) and Parzen Windows.
  • Dimensionality Reduction: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for feature selection.
  • Neural Networks: Multi-Layer Perceptron (MLP) with backpropagation.

Technologies used: Python, NumPy, Matplotlib, Scikit-learn, Pandas.