The project encompasses the statistical analysis of data using different clustering and feature selection techniques.
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Updated
Nov 1, 2021 - Python
The project encompasses the statistical analysis of data using different clustering and feature selection techniques.
Unsupervised Clustering of Global Palm Tree Species
Underwater Buoy detection using Gaussian Mixture Models (GMM) and Expectation-Maximization (EM) Algorithm
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.
Image analysis with Gaussian Mixture Model (GMM), with Principal Component Analysis (PCA) for dimensionality reduction of images prior to expectation-maximization (EM) algorithm implementation.
Unsupervised learning with different types clustering algorithms..
Clustering Case Study with Gaussian Mixture Models
Implemented the Principal Component Analysis (PCA) & performed dimensionality reduction. Implemented Hierarchical clustering EM algorithm for GMM and performed the clustering operations.
Gaussian Latent Dirichlet Allocation
Analyzing different clustering methods and finding the most suitable one
A UI for Sprocket-VC
Projects using Unsupervised Learning Techniques
This repository contains last project of UT-ML course files(feature extraction, classification, clustering)...
Image Clustering using PCA and GMM: A project implementing Principal Component Analysis and Gaussian Mixture Models for efficient image clustering.
clustering with optimal number of 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"
NUS Pattern Recognition module graded assignments
This repo is Homework-02 of EE-541(A Computational Introduction to Deep Learning) completed at USC.
Using Python for Data Science
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