274 episodes

In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications.

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    • Technology
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In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications.

    Putting machine learning into a database

    Putting machine learning into a database

    Most data scientists bounce back and forth regularly between doing analysis in databases using SQL and building and deploying machine learning pipelines in R or python. But if we think ahead a few years, a few visionary researchers are starting to see a world in which the ML pipelines can actually be deployed inside the database. Why? One strong advantage for databases is they have built-in features for data governance, including things like permissioning access and tracking the provenance of data. Adding machine learning as another thing you can do in a database means that, potentially, these enterprise-grade features will be available for ML models too, which will make them much more widely accepted across enterprises with tight IT policies. The papers this week articulate the gap between enterprise needs and current ML infrastructure, how ML in a database could be a way to knit the two closer together, and a proof-of-concept that ML in a database can actually work.

    Relevant links:
    https://blog.acolyer.org/2020/02/19/ten-year-egml-predictions/
    https://blog.acolyer.org/2020/02/21/extending-relational-query-processing/

    • 24 min
    The work-from-home episode

    The work-from-home episode

    Many of us have the privilege of working from home right now, in an effort to keep ourselves and our family safe and slow the transmission of covid-19. But working from home is an adjustment for many of us, and can hold some challenges compared to coming in to the office every day. This episode explores this a little bit, informally, as we compare our new work-from-home setups and reflect on what’s working well and what we’re finding challenging.

    • 29 min
    Understanding Covid-19 transmission: what the data suggests about how the disease spreads

    Understanding Covid-19 transmission: what the data suggests about how the disease spreads

    Covid-19 is turning the world upside down right now. One thing that’s extremely important to understand, in order to fight it as effectively as possible, is how the virus spreads and especially how much of the spread of the disease comes from carriers who are experiencing no or mild symptoms but are contagious anyway. This episode digs into the epidemiological model that was published in Science this week—this model finds that the data suggests that the majority of carriers of the coronavirus, 80-90%, do not have a detected disease. This has big implications for the importance of social distancing of a way to get the pandemic under control and explains why a more comprehensive testing program is critical for the United States.

    Also, in lighter news, Katie (a native of Dayton, Ohio) lays a data-driven claim for just declaring the University of Dayton flyers to be the 2020 NCAA College Basketball champions.

    Relevant links:
    https://science.sciencemag.org/content/early/2020/03/13/science.abb3221

    • 25 min
    Network effects re-release: when the power of a public health measure lies in widespread adoption

    Network effects re-release: when the power of a public health measure lies in widespread adoption

    This week’s episode is a re-release of a recent episode, which we don’t usually do but it seems important for understanding what we can all do to slow the spread of covid-19. In brief, public health measures for infectious diseases get most of their effectiveness from their widespread adoption: most of the protection you get from a vaccine, for example, comes from all the other people who also got the vaccine.

    That’s why measures like social distancing are so important right now: even if you’re not in a high-risk group for covid-19, you should still stay home and avoid in-person socializing because your good behavior lowers the risk for those who are in high-risk groups. If we all take these kinds of measures, the risk lowers dramatically. So stay home, work remotely if you can, avoid physical contact with others, and do your part to manage this crisis. We’re all in this together.

    • 26 min
    Causal inference when you can't experiment: difference-in-differences and synthetic controls

    Causal inference when you can't experiment: difference-in-differences and synthetic controls

    When you need to untangle cause and effect, but you can’t run an experiment, it’s time to get creative. This episode covers difference in differences and synthetic controls, two observational causal inference techniques that researchers have used to understand causality in complex real-world situations.

    • 20 min
    Better know a distribution: the Poisson distribution

    Better know a distribution: the Poisson distribution

    This is a re-release of an episode that originally ran on October 21, 2018.

    The Poisson distribution is a probability distribution function used to for events that happen in time or space. It’s super handy because it’s pretty simple to use and is applicable for tons of things—there are a lot of interesting processes that boil down to “events that happen in time or space.” This episode is a quick introduction to the distribution, and then a focus on two of our favorite everyday applications: using the Poisson distribution to identify supernovas and study army deaths from horse kicks.

    • 31 min

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