Machine Learning reading group, Exeter Business School

meeting_notes: rough notes coming after each session, feel free to add/edit/comment

papers-marked-up-and-comment-files: Pdfs of papers stored here; with notes and comments (?)

• BUT see hypothesis shared annotations below

Any shared 'developed' material could be put here.

I (David Reinstein) suggest that we all get an account for the Hypothesis tool, and install the browser/bookmark add-in. This tool is great (and free and nonprofit)! It takes less than 5 minutes to install and learn, and I guarantee you will start using it all the time. (I have no association with this company, I just like the tool).

• We can then leave comments and ask questions directly on any hosted web page on the internet, including the journal articles themselves.

  • Below I give a list of the papers with open-access (?) hosts where we can leave our comments/notes/questions. (If no single-click access is available I give my own dropbox link.)

• I suggest leaving your comments/notes with the tag ml-reading-group (no 'hash sign' needed). Then we can look up each others' comments anywhere on the internet. (And we can also use this to compile an organised writeup later if we like.)

• I suggest leaving these as public comments; down the road we may be able to engage experts and even the original authors in this.

• It allows math notation etc

Athey & Imbens (2019) - Machine Learning Methods Economists Should Know About

Hofman, Sharma & Watts (2017) - Prediction and explanation in social systems

[Kleinberg et al. (2015) - Prediction Policy Problems(https://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.p20151023)

Machine Learning: An Applied Econometric Approach- Sendhil Mullainathan and Jann Spiess (2017)