# 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/

**Understanding machine learning: From theory to algorithms**

(An introduction to statistical learning theory in the tradition of Vapnik)

https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

**Prediction, Learning, and Games**

(A principled adversarial (non-stochastic) framework for learning and online decision making)

https://cesa-bianchi.di.unimi.it/predbook/

**Introduction to Online Convex Optimization**

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

https://arxiv.org/abs/1909.05207v2

**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/

**The Ethical Algorithm**

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

https://global.oup.com/academic/product/the-ethical-algorithm-9780190948207

## Programming in R

**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/

**Hands-On Machine Learning with R**

(Fitting ML models in R)

https://bradleyboehmke.github.io/HOML/

**Bayesian statistics using Stan**

https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started

https://mc-stan.org/docs/2_20/stan-users-guide/index.html

**RStudio Cheat Sheets**

(for various extensions, including data processing, visualization, writing web apps, â€¦)

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

**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

**A Layered Grammar of Graphics**

(The theory behind ggplot2)

https://byrneslab.net/classes/biol607/readings/wickham_layered-grammar.pdf

**An Economistâ€™s Guide to Visualizing Data**

(Guidelines for good visualizations)

https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.28.1.209

## Typesetting

**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)

- Getting started: https://programminghistorian.org/en/lessons/building-static-sites-with-jekyll-github-pages
- The minimal-mistakes theme: https://mmistakes.github.io/minimal-mistakes/docs/quick-start-guide/

## Version control

**Git and Github for R**

https://happygitwithr.com/

**Git and Github, in depth**

https://git-scm.com/book/en/v2