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.

Modeling correlated count data

Implementation of Task-Parameterized-Gaussian-Mixture-Models as presented from S. Calinon in his paper: "A Tutorial on Task-Parameterized Movement Learning and Retrieval"

Machine Translation lab Implementation

A collection of the assignments in the course advanced machine learning

Code for the paper "Reconstructuring Sparse Multiplex Networks With Application to Covert Networks"

Spring 2021 Machine Learning (CS 181) Homework 5

Manual Implementation of some machine learning algorithms

This repository is for the ICML'24 paper: "Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression"

Repository for the code of the "Introduction to Machine Learning" (IML) lecture at the "Learning & Adaptive Systems Group" at ETH Zurich.

Language Invariant Optical Character Recognition

Gaussian Mixture Model in Python

Tools for Gaussian Mixture Models with Partially Labelled Data

Self-Organizing Maps on the GPU with Cupy