Useful computational ressources

Machine learning

The Elements of Statistical Learning
(General introduction to machine learning)
https://web.stanford.edu/~hastie/Papers/ESLII.pdf

Gaussian Processes for Machine Learning
(Extremely useful tools for nonparametric Bayesian modeling)
http://www.gaussianprocess.org/gpml/chapters/

Deep Learning
(The theory and implementation of neural nets)
https://www.deeplearningbook.org/

Reinforcement learning - An introduction
(Adaptive learning for Markov decision problems)
http://www.incompleteideas.net/book/RLbook2018.pdf

Algorithms
(Introduction to the theory of algorithms)
http://jeffe.cs.illinois.edu/teaching/algorithms/

An Introduction to R
(Complete introduction to base R)
https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf

R for Data Science
(Introduction to data analysis using R, focused on the tidyverse packages)
http://r4ds.had.co.nz/

Advanced R
(In depth discussion of programming in R)
https://adv-r.hadley.nz/

RStudio Cheat Sheets
(for various extensions, including data processing, visualization, writing web apps, …)
https://www.rstudio.com/resources/cheatsheets/

Programming interactive R-apps using Shiny
https://shiny.rstudio.com/articles/

Training neural nets using Keras and Tensorflow
https://tensorflow.rstudio.com/keras/

Data Visualization - A Practical Introduction
(Textbook on data visualization, using ggplot2)
http://socviz.co/

ggplot2 - Elegant Graphics for Data Analysis
(R-package for data vizualization)
http://moderngraphics11.pbworks.com/f/ggplot2-Book09hWickham.pdf

An Economist’s Guide to Visualizing Data
(Guidelines for good visualizations)
https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.28.1.209

The Not So Short Introduction to LATEX
https://tobi.oetiker.ch/lshort/lshort.pdf

Markdown
(a lightweight markup language)
https://www.markdownguide.org/

Setting up a webpage using Jekyll and Github pages
(Such as this webpage)

Git and Github for R
https://happygitwithr.com/

Git and Github, in depth
https://git-scm.com/book/en/v2