Inside a Data Analysis: Fraud Hunting at Enron
It's storytime this week--the story, from beginning to end, of how Katie designed and built the main project for Udacity's Intro to Machine Learning class, when she was developing the course. The pro...
16 Maj 201630min
What's the biggest #bigdata?
Data science and is often mentioned in the same breath as big data. But how big is big data? And who has the biggest big data? CERN? Youtube? ... Something (or someone) else? Relevant link: ht...
9 Maj 201625min
Data Contamination
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 Maj 201620min
Model Interpretation (and Trust Issues)
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
Updates! Political Science Fraud and AlphaGo
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
Ecological Inference and Simpson's Paradox
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
Discriminatory Algorithms
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
Recommendation Engines and Privacy
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 Mars 201631min
