Useful computational resources

Machine learning textbooks

The Elements of Statistical Learning (General introduction to machine learning)
https://web.stanford.edu/~hastie/Papers/ESLII.pdf
Patterns, Predictions, and Actions (Another introduction, with a focus on pattern classification, and an discussion of causal and dynamic frameworks)
https://mlstory.org/index.html
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
Learning Theory from First Principles (A rigorous overview of topics in current learning theory)
https://www.di.ens.fr/~fbach/ltfp_book.pdf

Gaussian Processes for Machine Learning (Very 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/
Speech and Language Processing (Natural language processing and language models)
https://web.stanford.edu/~jurafsky/slp3/

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

Data Visualization - A Practical Introduction (Textbook on data visualization, using ggplot2)
http://socviz.co/
An Economist’s Guide to Visualizing Data (Guidelines for good visualizations)
https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.28.1.209
A Layered Grammar of Graphics (The theory behind ggplot2)
https://byrneslab.net/classes/biol607/readings/wickham_layered-grammar.pdf

A Whirlwind Tour of Python (Quick introduction to the basics of Python)
https://github.com/jakevdp/WhirlwindTourOfPython
Python Data Science Handbook (In depth discussion of several packages for numerical computing and data science)
https://jakevdp.github.io/PythonDataScienceHandbook/

Polars (A library for wrangling data, very fast even for large datasets)
https://docs.pola.rs/
Scikit-learn (A comprehensive, consistent library for supervised machine learning)
https://scikit-learn.org/stable/
Vega-Altair (A declarative library for data visualization)
https://altair-viz.github.io/index.html
GreatTables (A library for producing formatted tables from data frames, in html, latex, etc.)
https://posit-dev.github.io/great-tables/articles/intro.html
Dash (A library for building web-apps in Python)
https://dash.plotly.com/

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/

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

The Not So Short Introduction to LATEX
https://tobi.oetiker.ch/lshort/lshort.pdf
Markdown (a lightweight markup language)
https://www.markdownguide.org/
Obsidian (great lightweight, hackable app using markdown, for note-taking, task-management, typesetting, …)
https://obsidian.md/
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/