Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language
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Updated
Jun 1, 2024 - R
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
Compute Convex (Bi)Clustering Solutions via Algorithmic Regularization
Regularized Multi-task Learning in R
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
Regularized logistic regressions with computational graphs
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