Python implementation of EM algorithm for GMM. And visualization for 2D case.

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

MS Yang, A robust EM clustering algorithm for Gaussian mixture models, Pattern Recognit., 45 (2012), pp. 3950-3961

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

Gaussian Mixture Model for Clustering

Python wrapper for R's Mclust algorithm: Gaussian model-based clustering with automatic model selection via BIC

ModelGaussian_Mixture_Model

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

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

Code for the paper Data-efficient model learning and prediction for contact-rich manipulation tasks, RA-L, 2020

RL and DMP algorithms implemented from scratch with plain Numpy.

2019～2020学年第2学期《并行程序设计》课程设计

Clustering algorithm implementaions from scratch with python (k-means, EM-GMM, mean-shift, agglomerative)

A recommender system based on data provided by MHRD on colleges and universities in India. Website-

Gaussian Latent Dirichlet Allocation

Expectation-Maximization (EM) algorithm for Gaussian mixture model (GMM) from scratch

A Python implementation of Gaussian Mixture Model (GMM)

Analyzing a dataset containing data on various customers' annual spending amounts of diverse product categories for internal structure. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.

Ozone profile clustering code for UKESM1

No description provided.

We are given 2 different problems to solve. 1. Isolated spoken digit recognition 2. Telugu Handwritten character recognition Both these datasets were given as a time series. 2 different methods were used to solve each of the problem: 1. Dynamic Time Warping 2. Hidden Markov Models

This project utilizes signal processing and machine learning techniques to analyze vibration data for detecting mechanical faults in rotating machinery. It includes the application of Fast Fourier Transform (FFT) for frequency analysis, feature extraction in both time and frequency domains, and classification using Support Vector Machines (SVM).

Fourth practical assignment for the course "I302 - ML and Deep Learning". The work consists of three problems involving clustering, dimensionality reduction and EM Algorithm.

Unsupervised Clustering of Global Palm Tree Species

This course covers fundamental concepts, methodologies, and algorithms related to machine learning taught by Fereydoon Vafaei

Recreation and enrichment of the gastric (GC) cancer single-cell RNA-seq (scRNA-seq) data analysis pipeline described in the "Comprehensive analysis of metastatic gastric cancer tumour cells using single‑cell RNA‑seq" by Wang B. et. al, using the raw counts matrix they provide.

This project clusters countries based on socio-economic factors using Gaussian Mixture Model (GMM). Input data like child mortality, income, etc., and get a prediction of whether a country is Poor Developing or Rich. The results are visualized on an interactive world map, allowing you to explore global clustering patterns.

Using GMM and KMeans implemented with julia do location prediction.

Gaussian Mixture Model with low rank approximation

Performed clustering analysis on OnSports player data for the English Premier League. The clustering analysis successfully identified 4 unique player clusters and uncovered valuable business recommendations by identifying trends and patterns in the EDA, meeting the objective of determining player pricing next season.