Pure Numpy Implementation of the Coherent Point Drift Algorithm

Fast clustering Expectation Maximization algorithm for Gaussian Mixture Models.

Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"

Explaining textual analysis tools in Python. Including Preprocessing, Skip Gram (word2vec), and Topic Modelling.

Animated Visualizations of Popular Machine Learning Algorithms

Code for the algorithms in the paper: Vaibhav B Sinha, Sukrut Rao, Vineeth N Balasubramanian. Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification. KDD WISDOM 2018

Notebooks explaining the intuition behind the Expectation Maximisation algorithm

Python library to implement advanced trading strategies using machine learning and perform backtesting.

Bayesian Methods for Machine Learning

Gaussian Mixture Model clustering using OpenCv Expectation Maximization implementation

An implementation of the expectation maximization algorithm

A LibreOffice Calc extension that clusters the rows in a table and color them to indicate the clusters.

Built text and image clustering models using unsupervised machine learning algorithms such as nearest neighbors, k means, LDA , and used techniques such as expectation maximization, locality sensitive hashing, and gibbs sampling in Python

SON-EM - Algorithm for parameter estimation of hybrid time-varying parameter systems using Sum of Norms regularization and Expectation Maximization

Code for "Variational Depth Search in ResNets" (https://arxiv.org/abs/2002.02797)

Modeling correlated count data

variational Bayesian algorithm for Brain MR image Segmentation

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

Learning Bayesian Network parameters using Expectation-Maximisation

Machine Learning UIUC SP 2018

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

Partial Label Learning based on GMM

Gaussian mixture modelling - Unsupervised learning

Fully supervised, multi-class 3D brain segmentation in T1 MRI, using atlas-based segmentation algorithms (label propagation, tissue models, Expectation-Maximization algorithm).

Data Mining and Machine Learning Algorithm Implementations

Fit binodal multidimention GMM with EM algorithm, an EM algorithm implementation

Code for our ICML '18 paper "Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization"