em-algorithm

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

Collection of EM algorithms for blind source separation of audio signals

A pytorch package for non-negative matrix factorization.

Implementation in Apache Spark of the EM algorithm to estimate parameters of Fellegi-Sunter's canonical model of record linkage.

Multivariate and Multichannel Discrete Hidden Markov Models for Categorical Sequences

R package to accompany Time Series Analysis and Its Applications: With R Examples -and- Time Series: A Data Analysis Approach Using R

Python machine learning applications in image processing and algorithm implementations including Matrix Completion, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

machine learning

Implementation of Unsupervised Naive Bayes with EM Algorithm

A Unified RNA Sequencing Model (URSM) for joint analysis of single cell and bulk RNA-seq data.

gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

Applied Machine Learning

Wine Types Clustering using K-Means, EM-GMM and PCA

Basic building blocks in Bayesian statistics.

Functional Latent datA Models for clusterING heterogeneOus curveS

Markov-Switching State-Space Models

Implementation of EM using K-Means(Gaussian Mixture Model)

Julia package for KF and EKF parameter estimation using Automatic Differentiation

Probabilistic graphical models home works (MVA - ENS Cachan)

Fully supervised binary classification of skin lesions from dermatoscopic images using multi-color space moments/texture features and Support Vector Machines/Random Forests.

This repository contains implementation of Neural Network,k-Means and Gaussian Mixture Models with Python

Combine partial covariance or relationship matrices

Use Gibbs sampling and variational inference to denoise the image and use EM to segment the image

Designing and applying unsupervised learning on the Radar signals to perform clustering using K-means and Expectation maximization for Gausian mixture models to study ionosphere structure. Both the algorithms have been implemented without the use of any built-in packages. The Dataset can be found here: https://archive.ics.uci.edu/ml/datasets/ionosphere

single-cell RNA sequencing imputation

This is the IR course of NTUST in 2017. IR means that Information Retrieval and Its Applications, including Vector Model, word2Vec technology and so on.

Finding of the missed values in the adjacency matrix of a big undirected weighted graph by utilizing probabilistic graphical models. The adjacency matrix's values were modeled with Poisson distribution and Gamma prior.