Maximilian Kasy

Maximilian Kasy

Professor of Economics, University of Oxford

Useful computational ressources

Machine learning

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

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

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

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/

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

Speech and Language Processing
(Natural language processing and language models)
https://web.stanford.edu/~jurafsky/slp3/

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 Python

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/

Dash Python User Guide
(A framework for quickly building web-apps in Python)
https://dash.plotly.com/

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, …)
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 Economist’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)

Version control

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

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