Non-homogenous Hidden Markov Models

Code to perform EM Clustering on Iris Dataset

Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)

Expectation-Maximization-based clustering algorithm to identify groups defined by biological variates as clusters in single-cell transcriptomic data.

The EM-BDD algorithm developed for learning Hidden Markov Models (HMMs) using Binary Decision Diagrams to optimize the learning process, offering a alternative to traditional methods such as the Baum-Welch algorithm.

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

An adversarial agent for playing Tic Tac Toe, offering users the option to choose board size and difficulty level. Utilizes various search algorithms including, but not limited to; Alpha-Beta Pruning, Expectimax, and Minimax. The project provides an interactive interface where users can play against the adversarial agent.

This repository implements K-Means clustering on the Old-Faithful dataset, with visualization of clustering iterations and distortion. It also applies K-Means for image compression/segmentation and utilizes the EM algorithm with a Gaussian Mixture Model for classification.

Modern computational statistics

Coding solutions to various image processing problems integrating statistical algorithm known as Expectation-Maximization (EM), and clustering algorithm known as Gaussian Mixture Model (GMM).

Image Clustering Algorithm implemented in C++

This repository hosts the programming exercises for the course "Machine Learning" of AUEB Informatics.

Plant skeleton optimization using stochastic framework on point cloud data.

mfair: Matrix Factorization with Auxiliary Information in R

This repository contains the code for a competitive assignment where we used Bayesian Networks for Medical Diagnosis as part of COL333- Principles of Artificial Intelligence

USC DSCI 552 - Machine Learning for Data Science - Spring 2023 - Prof. Ke-Thia Yao

Performance of the EM algorithm and imputation methods with different missing data mechanisms (EPFL - Statistical Computation and Visualization)

Implementation of Tractable Probabilistic Model Tree Bayesian Networks using Chow Liu Tree and Mixture of Trees using EM (Expectation-Maximization) algorithm and Mixture of Trees using Random Forest Technique in python

Machine Learning Algortihms from scratch.

Assignments in 'Applied Probabilistic Models' course by Prof. Ido Dagan at Bar-Ilan University.