Implementation of Image Processing Segmentation techniques and algorithms for Oil Spill detection in SAR images

Matlab pipeline for semi-supervised mouse behavioral classification

Andrew Ng's Machine Learning Course

Sparse simplex projection-based Wasserstein k-means

Machine learning problem sets from Stanford University's Machine Learning course on Coursera

Implementation of fundamental image processing algorithms using MATLAB

The implementation of our paper 't-k-means: A Robust and Stable k-means Variant', accepted by the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021.

This project implements three image segmentation algorithms - Region Growing, Watershed, and K-Means, to separate an object from its background, evaluated using the Jaccard Similarity Coefficient.

Several ML Algorithms implemented from scratch, without using inbuilt libraries. Regression Models, GDA, SVM, Naive Bayes, Decision Tree, PCA using SVD, Neural Network

This slightly modified k-means algorithm was written in Matlab as part of my thesis: "Denoising Hyperspectral Images by using Clustering Techniques with Point Representatives".

Machine learning with MATLAB/Octave, coding machine learning algorithms from scratch

Project on hyperspectral-image clustering for the Μ402 - Clustering Algorithms course, NKUA, Fall 2022.

Differential Evolution Clustering

https://www.coursera.org/account/accomplishments/records/XRTLLUWLMJ2T

Global Optimization for Cardinality-constrained Minimum Sum-of-Squares Clustering via Semidefinite Programming

MATLAB iterative algorithm that fits a n-dimensional simplicial complex to a point cloud in arbitrary dimensions based on a generalization of k-means clustering

A jigsaw puzzle solver term project.

A New Support Vector Finder Method, Based on Triangular Calculations and K-means Clustering

A simple program which performs K-Means clustering on a data set as well as visualizes the results.

A matlab implementation for the unsupervised learning algorithm ( K-Means).