Examples of machine learning and signal processing algorithms.

Gaussian Mixture Model for Clustering

Unsupervised Music Speech Classification using Gaussian Mixture Models (EM algorithm)

A Python implementation of Gaussian Mixture Model (GMM)

Repository pertaining to traditional statistical methods being applied to foreground segmentation for an image of a Cheetah

Gaussian mixture model for clustering

This repo consists of classification tasks using the Bayes classifier, GMMs, and nearest neighbor classifier.

Collection of classical density estimators e.g. mixture models, kernel density estimator etc.

🧩 The project explores computer vision techniques such as the Gaussian Mixture Models and U-Net deep learning model. In an effort to segment an image into foreground and background regions.

machine learning sessional works

Implementation of Expectation-Maximization (EM) algorithm for Gaussian Mixture Model

Implemented fundamental machine learning and statistical learning algorithms from scratch in Python to gain a deeper understanding of their mechanics. The focus is on building models without relying on built-in ML libraries and exploring the mathematics behind key algorithms. Tech: Python (numpy, pandas, seaborn, matplotlib, scipy, skmisc)

MNIST Classification using GMM and VGG

Underwater Buoy detection using Gaussian Mixture Models (GMM) and Expectation-Maximization (EM) Algorithm