October 11, 2020

Dear friends,
Welcome to the 72nd issue of the Sunday Briefing.

This week we have a new blog from the Causality series, where we look at Model Testing and Causal Search, the way in which we can validate our models and identify their equivalence class. As always, all the relevant code can be found in GitHub. This post completes Chapter 2 of the Primer just in time for the first edition of the Why and What If – Causal Analysis for Everyone webinar later this week. There’s still some available spots so go ahead and Register.

You can also checkout the latest in the Epidemic Modeling series where we go over the basics of Network Structure, Superspreaders and Contact Tracing.

In our regularly scheduled content, we have a look at how using Microsoft Excel caused Covid-19 results to be lost, Ten Research Challenge Areas in Data Science and some of the shortcommings of Machine Learning Education and a tutorial on How to design an algorithm.

On the academic front, we have a survey on Optimization Models for Machine Learning, an introduction on Causality for Machine Learning and, finally, A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks.

Finaly, in the video of the week, Jill Cates teaches us How to Design and Build a Recommendation System Pipeline in Python.

Data shows that the best way for a newsletter to grow is by word of mouth, so if you think one of your friends or colleagues would enjooy this newsletter, just go ahead and forward this email to them. This will help us spread the word!

Semper discentes,

The D4S Team