An interstitial word from Ben
A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.
5 Jun 20205min
Convolutional Neural Networks
This is a re-release of an episode that originally aired on April 1, 2018 If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural ...
31 Mai 202021min
Stein's Paradox
This is a re-release of an episode that was originally released on February 26, 2017. When you're estimating something about some object that's a member of a larger group of similar objects (say, th...
24 Mai 202027min
Protecting Individual-Level Census Data with Differential Privacy
The power of finely-grained, individual-level data comes with a drawback: it compromises the privacy of potentially anyone and everyone in the dataset. Even for de-identified datasets, there can be wa...
18 Mai 202021min
Causal Trees
What do you get when you combine the causal inference needs of econometrics with the data-driven methodology of machine learning? Usually these two don’t go well together (deriving causal conclusions ...
11 Mai 202015min
The Grammar Of Graphics
You may not realize it consciously, but beautiful visualizations have rules. The rules are often implict and manifest themselves as expectations about how the data is summarized, presented, and annota...
4 Mai 202035min
Gaussian Processes
It’s pretty common to fit a function to a dataset when you’re a data scientist. But in many cases, it’s not clear what kind of function might be most appropriate—linear? quadratic? sinusoidal? some co...
27 Apr 202020min
Keeping ourselves honest when we work with observational healthcare data
The abundance of data in healthcare, and the value we could capture from structuring and analyzing that data, is a huge opportunity. It also presents huge challenges. One of the biggest challenges is ...
20 Apr 202019min
