It's been a nasty flu season this year. So we were remembering a story from a few years back (but not covered yet on this podcast) about when Google tried to predict flu outbreaks faster than the Centers for Disease Control by monitoring searches and looking for spikes in searches for flu symptoms, doctors appointments, and other related terms. It's a cool idea, but after a few years turned into a cautionary tale of what can go wrong after Google's algorithm systematically overestimated flu inci...
Mar 26, 2018•13 min
This week's episodes is for data scientists, sure, but also for data science managers and executives at companies with data science teams. These folks all think very differently about the same question: what should a data science team be working on? And how should that decision be made? That's the subject of a talk that I (Katie) gave at Strata Data in early March, about how my co-department head and I select projects for our team to work on. We have several goals in data science project selecti...
Mar 19, 2018•31 min
Autoencoders are neural nets that are optimized for creating outputs that... look like the inputs to the network. Turns out this is a not-too-shabby way to do unsupervised machine learning with neural nets.
Mar 12, 2018•13 min
After all the back-patting around making data science datasets and code more openly available, we figured it was time to also dump a bucket of cold water on everyone's heads and talk about the things that can go wrong when data and code is a little too open. In this episode, we'll talk about two interesting recent examples: a de-identified medical dataset in Australia that was re-identified so specific celebrities and athletes could be matched to their medical records, and a series of military b...
Mar 05, 2018•26 min
A few weeks ago, we podcasted about a neural network that was being touted as "better than doctors" in diagnosing pneumonia from chest x-rays, and how the underlying dataset used to train the algorithm raised some serious questions. We're back again this week with further developments, as the author of the original blog post pointed us toward more developments. All in all, there's a lot more clarity now around how the authors arrived at their original "better than doctors" claim, and a number of...
Feb 26, 2018•35 min
One interesting trend we've noted recently is the proliferation of papers, articles and blog posts about data science that don't just tell the result--they include data and code that allow anyone to repeat the analysis. It's far from universal (for a timely counterpoint, read this article ), but we seem to be moving toward a new normal where data science conclusions are expected to be shown, not just told. Relevant links: https://github.com/fivethirtyeight/data https://blog.patricktriest.com/pol...
Feb 19, 2018•17 min
Building a machine learning system and maintaining it in production are two very different things. Some folks over at Google wrote a paper that shares their thoughts around all the items you might want to test or check for your production ML system. Relevant links: https://research.google.com/pubs/pub45742.html
Feb 12, 2018•20 min
We've already talked about neural nets in some detail (links below), and in particular we've been blown away by the way that image recognition from convolutional neural nets can be fed into recurrent neural nets that generate descriptions and captions of the images. Our episode today tells a similar tale, except today we're talking about a blog post where the author fed in wireframes of a website design and asked the neural net to generate the HTML and CSS that would actually build a website tha...
Feb 04, 2018•19 min
Last week we started the story of how you could use a machine learning model in place of a data structure, and this week we wrap up with an exploration of Bloom Filters and Hash Maps. Just like last week, when we covered B-trees, we'll walk through both the "classic" implementation of these data structures and how a machine learning model could create the same functionality.
Jan 29, 2018•21 min
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 lo...
Jan 22, 2018•19 min
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 t...
Jan 15, 2018•18 min
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.
Jan 08, 2018•16 min
What better way to kick off a new year than with an episode on the statistics of brewing beer?
Jan 02, 2018•15 min
We have a throwback episode for you today as we take the week off to enjoy the holidays. This week: what happens when you have a markov chain that generates mashup Kanye West lyrics with Bible verses? Exactly what you think.
Dec 24, 2017•10 min
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 o...
Dec 18, 2017•18 min
Picking up after last week's episode about maximal margin classifiers, this week we'll go into the kernel trick and how that (combined with maximal margin algorithms) gives us the much-vaunted support vector machine.
Dec 11, 2017•18 min
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!
Dec 04, 2017•14 min
Grab a cocktail, put on your favorite karaoke track, and let’s talk some more about disentangling audio data!
Nov 27, 2017•14 min
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!
Nov 20, 2017•16 min
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...
Nov 13, 2017•25 min
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 ...
Nov 06, 2017•22 min
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...
Oct 30, 2017•15 min
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 the...
Oct 23, 2017•17 min
It's the middle of October, so you've already made two pull requests to open source repos, right? If you have no idea what we're talking about, spend the next 20 minutes or so with us talking about the importance of open source software and how you can get involved. You can even get a free t-shirt! Hacktoberfest main page: https://hacktoberfest.digitalocean.com/#details
Oct 16, 2017•16 min
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 EVAR miles 13-17: no more ankle pain but quads start getting tight, it's a little more effort miles ...
Oct 09, 2017•18 min
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: https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf
Oct 02, 2017•19 min
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," ...
Sep 25, 2017•30 min
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.
Sep 18, 2017•28 min
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.
Sep 11, 2017•18 min
Software engineers are familiar with the idea of versioning code, so you can go back later and revive a past state of the system. For data scientists who might want to reconstruct past models, though, it's not just about keeping the modeling code. It's also about saving a version of the data that made the model. There are a lot of other benefits to keeping track of datasets, so in this episode we'll talk about data lineage or data provenance.
Sep 04, 2017•23 min
