The Case for "The Case for Learned Index Structures"

Jeff Dean and his collaborators at Google are turning the machine learning world upside down (again) with a recent paper about how machine learning models can be used as surprisingly effective substitutes for classic data structures. In this first part of a two-part series, we'll go through a data structure called b-trees. The structural form of b-trees make them efficient for searching, but if you squint at a b-tree and look at it a little bit sideways then the search functionality starts to look a little bit like a regression model--hence the relevance of machine learning models. If this sounds kinda weird, or we lost you at b-tree, don't worry--lots more details in the episode itself.

Relevant links:

Challenges with Using Machine Learning to Classify Chest X-Rays

Another installment in our "machine learning might not be a silver bullet for solving medical problems" series. This week, we have a high-profile blog post that has been making the rounds for the last few weeks, in which a neural network trained to visually recognize various diseases in chest x-rays is called into question by a radiologist with machine learning expertise. As it seemingly always does, it comes down to the dataset that's used for training--medical records assume a lot of context that may or may not be available to the algorithm, so it's tough to make something that actually helps (in this case) predict disease that wasn't already diagnosed. 

Relevant links:

Fourier Transforms

The Fourier transform is one of the handiest tools in signal processing for dealing with periodic time series data. Using a Fourier transform, you can break apart a complex periodic function into a bunch of sine and cosine waves, and figure out what the amplitude, frequency and offset of those component waves are. It's a really handy way of re-expressing periodic data--you'll never look at a time series graph the same way again.

Relevant links:

De-Biasing Word Embeddings

When we covered the Word2Vec algorithm for embedding words, we mentioned parenthetically that the word embeddings it produces can sometimes be a little bit less than ideal--in particular, gender bias from our society can creep into the embeddings and give results that are sexist. For example, occupational words like "doctor" and "nurse" are more highly aligned with "man" or "woman," which can create problems because these word embeddings are used in algorithms that help people find information or make decisions. However, a group of researchers has released a new paper detailing ways to de-bias the embeddings, so we retain gender info that's not particularly problematic (for example, "king" vs. "queen") while correcting bias.

Relevant links:

Maximal Margin Classifiers

Maximal margin classifiers are a way of thinking about supervised learning entirely in terms of the decision boundary between two classes, and defining that boundary in a way that maximizes the distance from any given point to the boundary.  It's a neat way to think about statistical learning and a prerequisite for understanding support vector machines, which we'll cover next week--stay tuned!

DBSCAN

DBSCAN is a density-based clustering algorithm for doing unsupervised learning.  It's pretty nifty: with just two parameters, you can specify "dense" regions in your data, and grow those regions out organically to find clusters.  In particular, it can fit irregularly-shaped clusters, and it can also identify outlier points that don't belong to any of the clusters. Pretty cool!

Relevant links:

Kaggle's "State of Data Science" Survey

Want to know what's going on in data science these days?  There's no better way than to analyze a survey with over 16,000 responses that recently released by Kaggle.  Kaggle asked practicing and aspiring data scientists about themselves, their tools, how they find jobs, what they find challenging about their jobs, and many other questions.  Then Kaggle released an interactive summary of the data, as well as the anonymized dataset itself, to help data scientists understand the trends in the data.  In this episode, we'll go through some of the survey toplines that we found most interesting and counterintuitive.

Relevant links:

Machine Learning Technical Debt

This week, we've got a fun paper by our friends at Google about the hidden costs of maintaining machine learning workflows.  If you've worked in software before, you're probably familiar with the idea of technical debt, which are inefficiencies that crop up in the code when you're trying to go fast.  You take shortcuts, hard-code variable values, skimp on the documentation, and generally write not-that-great code in order to get something done quickly, and then end up paying for it later on.  This is technical debt, and it's particularly easy to accrue with machine learning workflows.  That's the premise of this episode's paper.

Relevant links:

Improving Upon a First-Draft Data Science Analysis

There are a lot of good resources out there for getting started with data science and machine learning, where you can walk through starting with a dataset and ending up with a model and set of predictions.  Think something like the homework for your favorite machine learning class, or your most recent online machine learning competition.  However, if you've ever tried to maintain a machine learning workflow (as opposed to building it from scratch), you know that taking a simple modeling script and turning it into clean, well-structured and maintainable software is way harder than most people give it credit for.  That said, if you're a professional data scientist (or want to be one), this is one of the most important skills you can develop.

In this episode, we'll walk through a workshop Katie is giving at the Open Data Science Conference in San Francisco in November 2017, which covers building a machine learning workflow that's more maintainable than a simple script.  If you'll be at ODSC, come say hi, and if you're not, here's a sneak preview!

Survey Raking

It's quite common for survey respondents not to be representative of the larger population from which they are drawn.  But if you're a researcher, you need to study the larger population using data from your survey respondents, so what should you do?  Reweighting the survey data, so that things like demographic distributions look similar between the survey and general populations, is a standard technique and in this episode we'll talk about survey raking, a way to calculate survey weights when there are several distributions of interest that need to be matched.

Relevant links:

Re-release: Kalman Runners

In honor of the Chicago marathon this weekend (and due in large part to Katie recovering from running in it...) we have a re-release of an episode about Kalman filters, which is part algorithm part elaborate metaphor for figuring out, if you're running a race but don't have a watch, how fast you're going.

Katie's Chicago race report:

  • miles 1-13: light ankle pain, lovely cool weather, the most fun imaginable.
  • miles 13-17: no more ankle pain but quads start getting tight, it's a little more effort
  • miles 17-20: oof, really tight legs but still plenty of gas in then tank.
  • miles 20-23: it's warmer out now, legs hurt a lot but running through Pilsen and Chinatown is too fun to notice
  • mile 24: ugh cramp everything hurts
  • miles 25-26.2: awesome crowd support, really tired and loving every second

Final time: 3:54:35

Neural Net Dropout

Neural networks are complex models with many parameters and can be prone to overfitting.  There's a surprisingly simple way to guard against this: randomly destroy connections between hidden units, also known as dropout.  It seems counterintuitive that undermining the structural integrity of the neural net makes it robust against overfitting, but in the world of neural nets, weirdness is just how things go sometimes.

Relevant links:

Disciplined Data Science

As data science matures as a field, it's becoming clearer what attributes a data science team needs to have to elevate their work to the next level.  Most of our episodes are about the cool work being done by other people, but this one summarizes some thinking Katie's been doing herself around how to guide data science teams toward more mature, effective practices.  We'll go through five key characteristics of great data science teams, which we collectively refer to as "disciplined data science," and why they matter.

Relevant links:

Hurricane Forecasting

It's been a busy hurricane season in the Southeastern United States, with millions of people making life-or-death decisions based on the forecasts around where the hurricanes will hit and with what intensity.  In this episode we'll deconstruct those models, talking about the different types of models, the theory behind them, and how they've evolved through the years.  

Relevant links:

Spy Planes

There are law enforcement surveillance aircraft circling over the United States every day, and in this episode, we'll talk about how some folks at BuzzFeed used public data and machine learning to find them.  The fun thing here, in our opinion, is the blend of intrigue (spy planes!) with tech journalism and a heavy dash of publicly available and reproducible analysis code so that you (yes, you!) can see exactly how BuzzFeed identifies the surveillance planes.

Relevant links: