expectation-maximization

Pure Numpy Implementation of the Coherent Point Drift Algorithm

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

Fast clustering Expectation Maximization algorithm for Gaussian Mixture Models.

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

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

Bayesian Methods for Machine Learning

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

Animated Visualizations of Popular Machine Learning Algorithms

Notebooks explaining the intuition behind the Expectation Maximisation algorithm

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

By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

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

CLIP-seq Analysis of Multi-mapped reads

An implementation of the expectation maximization algorithm

Code and data for the KDD2020 paper "Learning Opinion Dynamics From Social Traces"

Gaussian Mixture Model clustering using OpenCv Expectation Maximization implementation

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

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

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

My notes for Prof. Klaus Obermayer's "Machine Intelligence 2 - Unsupervised Learning" course at the TU Berlin

Learning Bayesian Network parameters using Expectation-Maximisation

variational Bayesian algorithm for Brain MR image Segmentation

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

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

Modeling correlated count data

Machine Learning UIUC SP 2018