Kmeans, Kmeans++, Gaussian Mixtures

This project demonstrates the segmentation of images using a Gaussian Mixture Model (GMM) and the Expectation-Maximization (EM) algorithm. The project applies these advanced machine learning techniques to segment both grayscale and color images, providing a comprehensive approach to image segmentation.

Machine Translation lab Implementation

A collection of the assignments in the course advanced machine learning

This contains my course assignments of SEM 3

Language Invariant Optical Character Recognition

Clustering images using expectation maximization and k-means

Gaussian Mixture Model in Python

OpenCV Machine Learning samples

Tools for Gaussian Mixture Models with Partially Labelled Data

Underwater Buoy Detection using Colour Segmentation with Gaussian Mixture Model and Expectation Maximization

We explore and implement LDA in order to estimate topics on various datasets (text documents).

Homework Code for UCLA STATS 115 (Probabilistic Decision Making) Fall 22 Offering

Embedding LUR in Space State models

Solving NQueen and HeavyNqueen, Hill Climbing and Astar, based on Python, EM for Clustering

implementing k-means, GMMs from scratch