First known article by any renowned economist that focused on machine learning (and big data) was published in 2014 (Big Data: New Tricks for Econometrics by H. R. Varian). He introduced some applications of machine learning techniques such as decision trees, support vector machines, neural nets, and deep learning which allow for more effective ways to model complex relationships.
I believe that these methods have a lot to offer and should be more widely known and used by economists.
Here I will try my best to made a walk in through guide for the economists who are interested in machine learning. It should consists of some introductory concepts related to machine learning and mainly a guidelines for the new comers to this machine learning world (like me).
I found a very useful article here in Analytics Vidhya that portrait a basic overview of machine learning (ML) concepts and algorithms. Economics graduates should find it very easy to follow. Don’t worry about the Python or R codes there, just try to get an overview of the concepts and algorithms used in machine learning. You would find a lot of concepts are taken from econometrics, but most of them are not. Most of the ML techniques are developed by computer scientists. Michael Hochster, PhD in Statistics from Stanford; Director of Research at Pandora:
Computer scientists view machine learning as “algorithms for making good predictions.” Unlike statisticians, computer scientists are interested in the efficiency of the algorithms and often blur the distinction between the model and how the model is fit. Computer scientists are not too interested in how we got the data or in models as representations of some underlying truth. For them, machine learning is black boxes making predictions. And computer science has for the most part dominated statistics when it comes to making good predictions.