Linear Digressions
Ben Jaffe and Katie Malone
Linear Digressions is a podcast about machine learning and data science. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago.
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Top 10 Linear Digressions Episodes
Goodpods has curated a list of the 10 best Linear Digressions episodes, ranked by the number of listens and likes each episode have garnered from our listeners. If you are listening to Linear Digressions for the first time, there's no better place to start than with one of these standout episodes. If you are a fan of the show, vote for your favorite Linear Digressions episode by adding your comments to the episode page.
The Three Types of Data Scientists, and What They Actually Do
Linear Digressions
09/09/18 • 23 min
If you've been in data science for more than a year or two, chances are you've noticed changes in the field as it's grown and matured. And if you're newer to the field, you may feel like there's a disconnect between lots of different stories about what data scientists should know, or do, or expect from their job. This week, we cover two thought pieces, one that arose from interviews with 35(!) data scientists speaking about what their jobs actually are (and aren't), and one from the head of data science at AirBnb organizing core data science work into three main specialties. Relevant links: https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal
Limitations of Deep Nets for Computer Vision
Linear Digressions
11/18/18 • 27 min
Deep neural nets have a deserved reputation as the best-in-breed solution for computer vision problems. But there are many aspects of human vision that we take for granted but where neural nets struggle—this episode covers an eye-opening paper that summarizes some of the interesting weak spots of deep neural nets. Relevant links: https://arxiv.org/abs/1805.04025
Calibrated Models
Linear Digressions
02/06/17 • 14 min
Remember last week, when we were talking about how great the ROC curve is for evaluating models? How things change... This week, we're exploring calibrated risk models, because that's a kind of model that seems like it would benefit from some nice ROC analysis, but in fact the ROC AUC can steer you wrong there.
Regularization
Linear Digressions
10/03/16 • 17 min
Lots of data is usually seen as a good thing. And it is a good thing--except when it's not. In a lot of fields, a problem arises when you have many, many features, especially if there's a somewhat smaller number of cases to learn from; supervised machine learning algorithms break, or learn spurious or un-interpretable patterns. What to do? Regularization can be one of your best friends here--it's a method that penalizes overly complex models, which keeps the dimensionality of your model under control.
Conjoint Analysis: like AB testing, but on steroids
Linear Digressions
06/06/16 • 18 min
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
Gravitational Waves
Linear Digressions
02/15/16 • 20 min
All aboard the gravitational waves bandwagon--with the first direct observation of gravitational waves announced this week, Katie's dusting off her physics PhD for a very special gravity-related episode. Discussed in this episode: what are gravitational waves, how are they detected, and what does this announcement mean for future studies of the universe. Relevant links: http://www.nytimes.com/2016/02/12/science/ligo-gravitational-waves-black-holes-einstein.html https://www.ligo.caltech.edu/news/ligo20160211
Genetics and Um Detection (HMM Part 2)
Linear Digressions
03/25/15 • 14 min
In part two of our series on Hidden Markov Models (HMMs), we talk to Katie and special guest Francesco about more useful and novel applications of HMMs. We revisit Katie's "Um Detector," and hear about how HMMs are used in genetics research.
So long, and thanks for all the fish
Linear Digressions
07/26/20 • 35 min
All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years. It’s been a ride, and a real pleasure and privilege to talk to you each week. Thanks, best wishes, and good night! —Katie and Ben
Hunting for the Higgs
Linear Digressions
11/16/14 • 10 min
Machine learning and particle physics go together like peanut butter and jelly--but this is a relatively new development. For many decades, physicists looked through their fairly large datasets using the laws of physics to guide their exploration; that tradition continues today, but as ever-larger datasets get made, machine learning becomes a more tractable way to deal with the deluge. With this in mind, ATLAS (one of the major experiments at CERN, the European Center for Nuclear Research and home laboratory of the recently discovered Higgs boson) ran a machine learning contest over the summer, to see what advances could be found from opening up the dataset to non-physicists. The results were impressive--physicists are smart folks, but there’s clearly lots of advances yet to make as machine learning and physics learn from one another. And who knows--maybe more Nobel prizes to win as well! https://www.kaggle.com/c/higgs-boson
Watson
Linear Digressions
08/25/15 • 15 min
This machine learning algorithm beat the human champions at Jeopardy. What is... Watson?
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FAQ
How many episodes does Linear Digressions have?
Linear Digressions currently has 291 episodes available.
What topics does Linear Digressions cover?
The podcast is about Learning, Data, Podcasts, Technology and Science.
What is the most popular episode on Linear Digressions?
The episode title 'Rock the ROC Curve' is the most popular.
What is the average episode length on Linear Digressions?
The average episode length on Linear Digressions is 20 minutes.
How often are episodes of Linear Digressions released?
Episodes of Linear Digressions are typically released every 6 days, 23 hours.
When was the first episode of Linear Digressions?
The first episode of Linear Digressions was released on Nov 16, 2014.
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