Table of Contents

The idea here is to properly index my posts. However, I didn’t have time to finish yet.
This page is and will always be under construction!

Data sets
German Credit Data

Data pre-processing
Unsupervised data pre-processing: individual predictors
Near-zero variance predictors. Should we remove them?

Unsupervised Learning
PCA
Introduction to Principal Component Analysis (PCA)
Computing and visualizing PCA in R

Supervised Learning
Classification
Discriminant Analysis
Discriminant Analysis
Reduced-rank discriminant analysis
Computing and visualizing LDA in R
Regression
Linear Regression
Linear regression (according to Coursera’s ML course)
Logistic Regression
Logistic regression (according to Coursera’s ML course)
Spatial Modeling
Auto-logistic model
Latent Gaussian Models
Fast Bayesian Inference with INLA
Latent Gaussian Models and INLA

Web Scraping
The basics of XML for web-scraping

Decision theory
Declining marginal utility and the logarithmic utility function

R Software
Software development
devtools and testthat R packages, definitely worth using
Optimizing R with Multi-threaded OpenBLAS
Profiling R code
R scripts
Data handling
Reshape and aggregate data with the R package reshape2
Visualization
Plot matrix with the R package GGally
Text Analysis
Character strings in R

Statistical Models

Approximate Methods for Statistical Inference

Model Selection and Model Assessment

Numerics

Other Statistical Concepts

Tools/Software

– R
-UNIX-like
– Others

Book summaries and/or comments

– The elements of statistical learning: data mining, inference and prediction, by Trevor Hastie, Robert Tibshirani and Jerome Friedman (book link)
– Coursera’s Machine Learning course, by Andrew Ng (course link)

Uncategorized (yet)

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