SLIDES: MACHINE LEARNING BRIEF OVERVIEW

This 60 slide overview was presented June 2019
machlearn2019_Intro_brief.pdf

SLIDES: CAUSAL MACHINE LEARNING FOR ECONOMICS BRIEF OVERVIEW

SLIDES: MORE DETAIL ON MACHINE LEARNING IN GENERAL

The following two sets of slides provide much more detail on basic machine learning methods.
They were created in April 2019 for short courses in Germany
machlearn2019_part1.pdf   (Basics: selection, shrinkage, dimension reduction, LASSO)
machlearn2019_part2.pdf   (Flexible methods: including random forests, classification and cluster analysis)

The following set of slides provides much more detail on use in economics of machine learning methods.
These slides were created in April 2019 for short courses in Germany and presentation at U.C. Riverside.
They cover a prediction example in economics and then various methods for causal inference in the partially linear model and in heterogeneous effects models.
The slides also list key references in the current economics literature.
machlearn2019_Riverside_2.pdf

For statistical learning the main text used in 240F is an undergraduate / masters level book
ISL: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibsharani (2013), An Introduction to Statistical Learning: with Applications in R, Springer.
A free legal pdf is at http://www-bcf.usc.edu/~gareth/ISL/ and a $25 hardcopy can be obtained via http://www.springer.com/gp/products/books/mycopy

Supplementary material on statistical learning came from the Ph.D. level book
ESL: Trevor Hastie, Robert Tibsharani and Jerome Friedman (2009), The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer.
A free legal pdf is at http://statweb.stanford.edu/~tibs/ElemStatLearn/index.html and a $25 hardcopy can be obtained via http://www.springer.com/gp/products/books/mycopy