Linear Digressions

Supervised machine learning assumes that the features and labels used for building a classifier are isolated from each other--basically, that you can't cheat by peeking. Turns out this can be easier ...

2 Mai 201620min

Machine learning algorithms can be black boxes--inputs go in, outputs come out, and what happens in the middle is anybody's guess. But understanding how a model arrives at an answer is critical for i...

25 Apr 201616min

We've got updates for you about topics from past shows! First, the political science scandal of the year 2015 has a new chapter, we'll remind you about the original story and then dive into what has ...

18 Apr 201631min

Simpson's paradox is the data science equivalent of looking through one eye and seeing a very clear trend, and then looking through the other eye and seeing the very clear opposite trend. In one case...

11 Apr 201618min

Sometimes when we say an algorithm discriminates, we mean it can tell the difference between two types of items. But in this episode, we'll talk about another, more troublesome side to discrimination...

4 Apr 201615min

This episode started out as a discussion of recommendation engines, like Netflix uses to suggest movies. There's still a lot of that in here. But a related topic, which is both interesting and impor...

28 Mar 201631min

One neural net is creating counterfeit bills and passing them off to a second neural net, which is trying to distinguish the real money from the fakes. Result: two neural nets that are better than ei...

21 Mar 201618min

In this episode, we're taking many episodes' worth of insights and unpacking an extremely complex and important question--in what ways are we winning the fight against cancer, where might that fight g...

14 Mar 201619min