Empirical Bayes
Say you're looking to use some Bayesian methods to estimate parameters of a system. You've got the normalization figured out, and the likelihood, but the prior... what should you use for a prior? Em...
20 Feb 201718min
Endogenous Variables and Measuring Protest Effectiveness
Have you been out protesting lately, or watching the protests, and wondered how much effect they might have on lawmakers? It's a tricky question to answer, since usually we need randomly distributed ...
13 Feb 201716min
Calibrated Models
Remember last week, when we were talking about how great the ROC curve is for evaluating models? How things change... This week, we're exploring calibrated risk models, because that's a kind of mode...
6 Feb 201714min
Rock the ROC Curve
This week: everybody's favorite WWII-era classifier metric! But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs.
30 Jan 201715min
Ensemble Algorithms
If one machine learning model is good, are two models better? In a lot of cases, the answer is yes. If you build many ok models, and then bring them all together and use them in combination to make ...
23 Jan 201713min
How to evaluate a translation: BLEU scores
As anyone who's encountered a badly translated text could tell you, not all translations are created equal. Some translations are smooth, fluent and sound like a poet wrote them; some are jerky, non-...
16 Jan 201717min
Zero Shot Translation
Take Google-size data, the flexibility of a neural net, and all (well, most) of the languages of the world, and what you end up with is a pile of surprises. This episode is about some interesting fea...
9 Jan 201725min
Google Neural Machine Translation
Recently, Google swapped out the backend for Google Translate, moving from a statistical phrase-based method to a recurrent neural network. This marks a big change in methodology: the tried-and-true ...
2 Jan 201718min
