Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

Software for learning sparse Bayesian networks

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

Now on CRAN, bigKRLS combines bigmemory & RcppArmadillo (C++) for speed into a new Kernel Regularized Least Squares algorithm. Slides:

A document covering machine learning basics. 🤖📊

MoMA: Modern Multivariate Analysis in R

Regularized Multi-task Learning in R

Compute Convex (Bi)Clustering Solutions via Algorithmic Regularization

R Package: Regularized Principal Component Analysis for Spatial Data

📊 Generalized linear regression models with network-regularization in R.

Regularized and Pruned Extreme Learning Machines in R

A quick reference for how to run many models in R.

lessSEM estimates sparse structural equation models.

Использование методов машинного обучения для прогнозирования инвестиций в России

Generalized Linear Models with the Exclusive Lasso Penalty

An R package inspired by 'mixup: Beyond Empirical Risk Minimization'

Network-Based Regularization for Generalized Linear Models

Machine Learning exercises for my subject of Machine Learning at University of Granada (UGR).

R package for regularization and change-point analysis in time series and panel data using Bayesian inference

My Master's thesis on Bayesian Classification with Regularized Gaussian Models