Linear Digressions

Latest Episodes:

SMOTE: makin' yourself some fake minority data Jun 13, 2016

Show notes

Machine learning on imbalanced classes: surprisingly tricky. Many (most?) algorithms tend to just assign the majority class label to all the data and call it a day. SMOTE is an algorithm for manufacturing new minority class examples for yourself, to help your algorithm better identify them in the wild. Relevant links: https://www.jair.org/media/953/live-953-2037-jair.pdf

Conjoint Analysis: like AB testing, but on steroids Jun 06, 2016

Show notes

Conjoint analysis is like AB tester, but more bigger more better: instead of testing one or two things, you can test potentially dozens of options. Where might you use something like this? Well, if you wanted to design an entire hotel chain completely from scratch, and to do it in a data-driven way. You'll never look at Courtyard by Marriott the same way again. Relevant link: https://marketing.wharton.upenn.edu/files/?whdmsaction=public:main.file&fileID=466

Traffic Metering Algorithms May 30, 2016

Show notes

This episode is for all you (us) traffic nerds--we're talking about the hidden structure underlying traffic on-ramp metering systems. These systems slow down the flow of traffic onto highways so that the highways don't get overloaded with cars and clog up. If you're someone who listens to podcasts while commuting, and especially if your area has on-ramp metering, you'll never look at highway access control the same way again (yeah, we know this is super nerdy; it's also super awesome). Relevant links: http://its.berkeley.edu/sites/default/files/publications/UCB/99/PWP/UCB-ITS-PWP-99-19.pdf http://www.its.uci.edu/~lchu/ramp/Final_report_mou3013.pdf

Um Detector 2: The Dynamic Time Warp May 23, 2016

Show notes

One tricky thing about working with time series data, like the audio data in our "um" detector (remember that? because we barely do...), is that sometimes events look really similar but one is a little bit stretched and squeezed relative to the other. Besides having an amazing name, the dynamic time warp is a handy algorithm for aligning two time series sequences that are close in shape, but don't quite line up out of the box. Relevant link: http://www.aaai.org/Papers/Workshops/1994/WS-94-03/WS94-03-031.pdf

Inside a Data Analysis: Fraud Hunting at Enron May 16, 2016

Show notes

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 project was to use email and financial data to hunt for signatures of fraud at Enron, one of the biggest cases of corporate fraud in history; that description makes the project sound pretty clean but getting the data into the right shape, and even doing some dataset merging (that hadn't ever been done before), made this project much more interesting to design than it might appear. Here's the story of what a data analysis like this looks like...from the inside.

What's the biggest #bigdata? May 09, 2016

Show notes

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: http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002195

Data Contamination May 02, 2016

Show notes

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 said than done. In this episode, we'll talk about the many (and diverse!) cases where label information contaminates features, ruining data science competitions along the way. Relevant links: https://www.researchgate.net/profile/Claudia_Perlich/publication/221653692_Leakage_in_data_mining_Formulation_detection_and_avoidance/links/54418bb80cf2a6a049a5a0ca.pdf

Model Interpretation (and Trust Issues) Apr 25, 2016

Show notes

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 interpreting the model, and for knowing if it's doing something reasonable (one could even say... trustworthy). We'll talk about a new algorithm called LIME that seeks to make any model more understandable and interpretable. Relevant Links: http://arxiv.org/abs/1602.04938 https://github.com/marcotcr/lime/tree/master/lime

Updates! Political Science Fraud and AlphaGo Apr 18, 2016

Show notes

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 happened since. Then, we've got an update on AlphaGo, and his/her/its much-anticipated match against the human champion of the game Go. Relevant Links: https://soundcloud.com/linear-digressions/electoral-insights-part-2 https://soundcloud.com/linear-digressions/go-1 http://www.sciencemag.org/news/2016/04/talking-people-about-gay-and-transgender-issues-can-change-their-prejudices http://science.sciencemag.org/content/sci/352/6282/220.full.pdf http://qz.com/639952/googles-ai-won-the-game-go-by-defying-millennia-of-basic-human-instinct/ http://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/ http://www.wired.com/2016/03/sadness-beauty-watching-googles-ai-play-go/

Ecological Inference and Simpson's Paradox Apr 11, 2016

Show notes

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, you see a trend one way in a group, but then breaking the group into subgroups gives the exact opposite trend. Confused? Scratching your head? Welcome to the tricky world of ecological inference. Relevant links: https://gking.harvard.edu/files/gking/files/part1.pdf http://blog.revolutionanalytics.com/2013/07/a-great-example-of-simpsons-paradox.html

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