Linear Digressions
In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications.
- Update frequency
- every 6 days
- Average duration
- 19 minutes
- Episodes
- 291
- Years Active
- 2014 - 2020
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 …
00:26:40 |
Sun 15 Mar 2020
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 …
00:20:48 |
Mon 09 Mar 2020
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 supe…
00:31:51 |
Mon 02 Mar 2020
The Lottery Ticket Hypothesis
Recent research into neural networks reveals that sometimes, not all parts of the neural net are equally responsible for the performance of the network overall. Instead, it seems like (in some neura…
00:19:45 |
Sun 23 Feb 2020
Interesting technical issues prompted by GDPR and data privacy concerns
Data privacy is a huge issue right now, after years of consumers and users gaining awareness of just how much of their personal data is out there and how companies are using it. Policies like GDPR ar…
00:20:26 |
Mon 17 Feb 2020
Thinking of data science initiatives as innovation initiatives
Put yourself in the shoes of an executive at a big legacy company for a moment, operating in virtually any market vertical: you’re constantly hearing that data science is revolutionizing the world an…
00:17:27 |
Mon 10 Feb 2020
Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng
As demand for data scientists grows, and it remains as relevant as ever that practicing data scientists have a solid methodological and technical foundation for their work, higher education instituti…
00:31:36 |
Sun 02 Feb 2020
Running experiments when there are network effects
Traditional A/B tests assume that whether or not one person got a treatment has no effect on the experiment outcome for another person. But that’s not a safe assumption, especially when there are net…
00:24:45 |
Mon 27 Jan 2020
Zeroing in on what makes adversarial examples possible
Adversarial examples are really, really weird: pictures of penguins that get classified with high certainty by machine learning algorithms as drumsets, or random noise labeled as pandas, or any one o…
00:22:51 |
Mon 20 Jan 2020
Unsupervised Dimensionality Reduction: UMAP vs t-SNE
Dimensionality reduction redux: this episode covers UMAP, an unsupervised algorithm designed to make high-dimensional data easier to visualize, cluster, etc. It’s similar to t-SNE but has some advant…
00:29:34 |
Mon 13 Jan 2020
Data scientists: beware of simple metrics
Picking a metric for a problem means defining how you’ll measure success in solving that problem. Which sounds important, because it is, but oftentimes new data scientists only get experience with a …
00:24:47 |
Sun 05 Jan 2020
Communicating data science, from academia to industry
For something as multifaceted and ill-defined as data science, communication and sharing best practices across the field can be extremely valuable but also extremely, well, multifaceted and ill-defin…
00:26:15 |
Mon 30 Dec 2019
Optimizing for the short-term vs. the long-term
When data scientists run experiments, like A/B tests, it’s really easy to plan on a period of a few days to a few weeks for collecting data. The thing is, the change that’s being evaluated might have…
00:19:24 |
Mon 23 Dec 2019
Interview with Prof. Andrew Lo, on using data science to inform complex business decisions
This episode features Prof. Andrew Lo, the author of a paper that we discussed recently on Linear Digressions, in which Prof. Lo uses data to predict whether a medicine in the development pipeline wi…
00:27:46 |
Mon 16 Dec 2019
Using machine learning to predict drug approvals
One of the hottest areas in data science and machine learning right now is healthcare: the size of the healthcare industry, the amount of data it generates, and the myriad improvements possible in th…
00:25:00 |
Sun 08 Dec 2019
Facial recognition, society, and the law
Facial recognition being used in everyday life seemed far-off not too long ago. Increasingly, it’s being used and advanced widely and with increasing speed, which means that our technical capabilitie…
00:43:09 |
Mon 02 Dec 2019
Lessons learned from doing data science, at scale, in industry
If you’ve taken a machine learning class, or read up on A/B tests, you likely have a decent grounding in the theoretical pillars of data science. But if you’re in a position to have actually built lo…
00:28:00 |
Mon 25 Nov 2019
Varsity A/B Testing
When you want to understand if doing something causes something else to happen, like if a change to a website causes and dip or rise in downstream conversions, the gold standard analysis method is to…
00:36:00 |
Mon 18 Nov 2019
The Care and Feeding of Data Scientists: Growing Careers
In the third and final installment of a conversation with Michelangelo D’Agostino, VP of Data Science and Engineering at Shoprunner, about growing and mentoring data scientists on your team. Some of …
00:25:19 |
Mon 11 Nov 2019
The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists
This week’s episode is the second in a three-part interview series with Michelangelo D’Agostino, VP of Data Science at Shoprunner. This discussion centers on building a team, which means recruiting, …
00:20:16 |
Mon 04 Nov 2019
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