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

• Auto-logistic model (18/01/2013)
• First two weeks of Coursera’s Machine Learning (linear regression) (08/05/2013)
• Third week of Coursera’s Machine Learning (logistic regression) (15/05/2013)
• 4th and 5th week of Coursera’s Machine Learning (neural networks) (05/06/2013)
• Bayesian linear regression model – simple, yet useful results (07/08/2013)
• Latent Gaussian Models and INLA (16/10/2013)

Approximate Methods for Statistical Inference

• INLA group (04/02/2013)
• Introduction to Variational Bayes (31/07/2013)
• Fast Bayesian Inference with INLA (09/10/2013)
• Latent Gaussian Models and INLA (16/10/2013)

Model Selection and Model Assessment

• Bias-variance trade-off in model selection (10/04/2013)
• Overview of Supervised Learning according to (Hastie et. al., 2009) (24/04/2013)
• AIC, Kullback-Leibler and a more general Information Criterion (01/05/2013)
• Model selection and model assessment according to (Hastie and Tibshirani, 2009) – Part [1/3] (22/05/2013)
• Model selection and model assessment according to (Hastie and Tibshirani, 2009) – Part [2/3] (29/05/2013)
• Model selection and model assessment according to (Hastie and Tibshirani, 2009) – Part [3/3] (19/06/2013)
• How to properly assess point forecast (03/07/2013)
• 6th week of Coursera’s Machine Learning (advice on applying machine learning) (17/07/2013)
• Posterior predictive checks (28/08/2013)
• 6th week of Coursera’s Machine Learning (Error analysis) (18/09/2013)

Numerics

• Fast, simple and useful numerical integration methods (02/10/2013)
• Numerical computation of quantiles (23/10/2013)

Other Statistical Concepts

• Kullback-Leibler divergence (10/07/2013)

Tools/Software

– R

• ODB R package (02/04/2013)
• devtools and testthat R packages, definitely worth using (26/06/2013)
• Optimizing R with Multi-threaded OpenBLAS (21/08/2013)
• Profiling R code (25/09/2013)
• Latent Gaussian Models and INLA (16/10/2013)
• Numerical computation of quantiles (23/10/2013)
• Reshape and aggregate data with the R package reshape2 (31/10/2013)
• Unsupervised data pre-processing for predictive modeling (07/11/2013)

-UNIX-like

• Run long computations remotely with screen (22/01/2013)
• Scheduling R scripts to run on a regular basis (11/09/2013)

– Others

• How to draw neural network diagrams using Graphviz (12/06/2013)
• Using Dropbox as a private git repository (24/07/2013)
• LaTeX and WordPress.com (14/08/2013)
• The basics of XML for web-scraping (04/09/2013)

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)

• Overview of Supervised Learning according to (Hastie et. al., 2009) (24/04/2013)
• Model selection and model assessment according to (Hastie and Tibshirani, 2009) – Part [1/3] (22/05/2013)
• Model selection and model assessment according to (Hastie and Tibshirani, 2009) – Part [2/3] (29/05/2013)
• Model selection and model assessment according to (Hastie and Tibshirani, 2009) – Part [3/3] (19/06/2013)

– Coursera’s Machine Learning course, by Andrew Ng (course link)

• First two weeks of Coursera’s Machine Learning (linear regression) (08/05/2013)
• Third week of Coursera’s Machine Learning (logistic regression) (15/05/2013)
• 4th and 5th week of Coursera’s Machine Learning (neural networks) (05/06/2013)
• 6th week of Coursera’s Machine Learning (advice on applying machine learning) (17/07/2013)
• 6th week of Coursera’s Machine Learning (Error analysis) (18/09/2013)

Uncategorized (yet)

• How much gold is there in the world? (02/04/2013)
• College education. Is it for everyone? (05/04/2013)
• The $100 Startup – Guidelines to set your own microbusiness (09/04/2013)
• Productivity Paradox and Prediction Failures (21/04/2013)