Machinelearning_algorithms_scratch

Bayesian linear and Gaussian process regression to predict CO2 concentration as a function of time

Engineering Thesis written to predict fault detection in wind turbines using Python

interactive gaussian process modelling with d3.js

AvGPR is a package that calculates a weighted average Gaussian Process regression model over 5 implementations from packages in both R and Python.

Expectation Maximisation, Variational Bayes, ARD, Loopy Belief Propagation, Gaussian Process Regression

An implementation of 4 machine learning algorithms from scratch

Available R-Packages for Gaussian Process Regression

Hierarchical Gaussian Processes based Multi-Robot Relative Localization

Study of Gaussian Process (GP) local and global approximations, and application of the sparse GP approximation, combining both the global and local approaches.

This repository contains Jupyter Notebook file containing the code to compare different sklearn classifiers on a dataset. Then it saves the output .png results in the working folder.

Gaussian Process Regression (GPR) with non-Gaussian likelihoods using robust infinite-dimension Monte Carlo Markov Chain (MCMC) sampling for spatial inference problems

Gaussian Processes for Machine Learning

Model for 2019 March Madness, 37th place on Kaggle + Google Cloud & NCAA ML Competition 2019

Infinite-width neural networks from a practical point of view

A C++ implementation of Deep Gaussian Process with Stochastic Imputation

Ko, Jongwoo, and Heeyoung Kim. "Deep Gaussian Process Models for Integrating Multifidelity Experiments with Non-stationary Relationships." IISE Transactions just-accepted (2021): 1-28.

A Neural Network that predicts the direction of a force experienced by the Syntouch Biotac robotic finger

Contribution to an open source repository which implements the Bayesian Optimization algorithm - Knowledge Gradient implementation