Machine Learning for Economists: An Introduction

Sonan Memon

Abstract


Machine Learning (henceforth ML) refers to the set of
algorithms and computational methods which enable computers to learn
patterns from training data without being explicitly programmed to do
so. ML uses training data to learn patterns by estimating a mathematical
model and making predictions in out of sample based on new or unseen
input data. ML has the tremendous capacity to discover complex, flexible
and crucially generalisable structure in training data. Conceptually
speaking, ML can be thought of as a set of complex function
approximation techniques which help us learn the unknown and potentially
highly nonlinear mapping between the data and prediction outcomes,
outperforming traditional techniques. 1 In this exposition, my aim is to
provide a basic and non-technical overview of 2 machine learning and its
applications for economists including development economists. For more
technical and complete treatments, you may consult Alpaydin (2020) and
James, et al. (2013). You may also wish to refer to my four lecture
series on machine learning on YouTube https://
www.youtube.com/watch?v=E9dLEAZW3L4 and my GitHub page for detailed and
more technical lecture slides
https://github.com/sonanmemon/Introductionto-ML-For-Economists.

Full Text:

PDF


DOI: https://doi.org/10.30541/v60i2pp.201-211

Refbacks

  • There are currently no refbacks.