Useful computational ressources

Machine learning

The Elements of Statistical Learning
(General introduction to machine learning)

Gaussian Processes for Machine Learning
(Extremely useful tools for nonparametric Bayesian modeling)

Deep Learning
(The theory and implementation of neural nets)

Understanding machine learning: From theory to algorithms
(An introduction to statistical learning theory in the tradition of Vapnik)

Prediction, Learning, and Games
(A principled adversarial (non-stochastic) framework for learning and online decision making)

Introduction to Online Convex Optimization
An accessible introduction to adversarial learning and adaptive decision-making through the lens of optimization theory.

Reinforcement learning - An introduction
(Adaptive learning for Markov decision problems)

(Introduction to the theory of algorithms)

The Ethical Algorithm
(How to impose normative constraints on ML and other algorithms)

Programming in R

An Introduction to R
(Complete introduction to base R)

R for Data Science
(Introduction to data analysis using R, focused on the tidyverse packages)

Advanced R
(In depth discussion of programming in R)

Hands-On Machine Learning with R
(Fitting ML models in R)

Bayesian statistics using Stan

RStudio Cheat Sheets
(for various extensions, including data processing, visualization, writing web apps, …)

Programming interactive R-apps using Shiny

Training neural nets using Keras and Tensorflow

Data visualization

Data Visualization - A Practical Introduction
(Textbook on data visualization, using ggplot2)

ggplot2 - Elegant Graphics for Data Analysis
(R-package for data vizualization)

A Layered Grammar of Graphics
(The theory behind ggplot2)

An Economist’s Guide to Visualizing Data
(Guidelines for good visualizations)


The Not So Short Introduction to LATEX

(a lightweight markup language)

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

Version control

Git and Github for R

Git and Github, in depth