Course Material for Artificial Intelligence and Machine Learning - Unit 2 @ Computer Science Dept, Sapienza

Implementing PCA on MNIST and then performing GMM clustering. PCA is performed from scratch and done for 32, 64, and 128 components. Clustering performed in 10, 7, and 4 clusters.

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

Regression, Classification, Clustering, Dimension-reduction, Anomaly detection

207 Machine Learning Project using various clustering models

clustering with optimal number of clusters

A collection of fundamental Machine Learning Algorithms Implemented from scratch along-with their applications for various ML tasks like clustering, thresholding, data analysis, prediction, regression and image classification.

Personal research project on examining different types of clustering algorithms, as well as investigate their impact when they are used in classification problem (cluster-based feature engineering)

Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.

Implemented the Principal Component Analysis (PCA) & performed dimensionality reduction. Implemented Hierarchical clustering EM algorithm for GMM and performed the clustering operations.

Image Clustering using PCA and GMM: A project implementing Principal Component Analysis and Gaussian Mixture Models for efficient image clustering.

ModelGaussian_Mixture_Model

This repository is for sharing the scripts of EM algorithm and variational bayes.

Using Python for Data Science

Statistical Model (GMM) employed for Audio Classification

This repository has been created just for warm-up in machine learning and there are my simulation files of UT-ML course HWs.

This repository contains last project of UT-ML course files(feature extraction, classification, clustering)...

This an unsupervised learning project analyzing single cell RNA seq data with Gaussian Mixture Models.