Podcasts about ML
There are a lot of podcasts out there, and this includes loads that are related to machine learning in some way. Over the summer I’ve worked my way through a fair few of them, so I thought I’d compile a list of my favorites. There are a couple of lists like this out there already, but most of them are out of date, either they include podcasts that are now inactive, or leave out ones that have started recently. I’m going to roughly work from the ones that I have listened to more, to the ones that I started recently. They’re not all strictly about ML but are in the general ML/AI/data science/statistics area. Happy listening!
Talking Machines⌗
The gold standard for ML podcasting! It’s hosted by Kathrine Gorman and Neil Lawrence, and is a really nice blend of ML theory and interviews with researchers in the field. Generally the first half of the podcast is just the hosts discussing an idea or concept in depth, anything from Hamiltonian Monte Carlo to Data Trusts, and the second half is an interview. They get a really nice mix of guests on, from lots of different areas, both industry and academia. Each guest is always asked how they got to where they are today, it’s super interesting to hear the answers, never the same route twice. There’s always lots to learn and a bit of a laugh also, can personally endorse every episode (well worth digging into the back catalogue also, Ryan Adams previously hosted instead of Neil)!
Not So Standard Deviations⌗
This is definitely more on the data science/stats side of things, and is hosted by Hilary Parker and Roger Peng. They mostly discuss non-technical topics, related to life as data scientists, and there are a lot of interesting tangents into random topics. I’ve particularly enjoy their discussion of ethics in statistics, as well as their general distain for hype. Most of the technical/practical discussion is R related, so if that’s what you’re into look no further 1.
Underrated ML⌗
A relatively new podcast hosted by brother and sister, Shaun and Sara Hooker, the concept is as follows, each week a guest is invited to discuss a paper that they think is underrated by the ML community, either Sara or Sean then presents a paper of their own, and the listeners decide which paper is the most underrated. It’s great fun to listen to, plenty of laughs, but also has deep technical discussions. The guests are great as well, it’s quickly become one of my favorite podcasts.
TWIML⌗
A weekly podcast with Sam Charrington, in which he interviews a huge variety of people with some connection to ML. The are loads of episode of this, so I mostly pick and choose the ones with people I’m interested in. Sam is a good interviewer, and gets the most out of the guests. I particularly enjoyed a recent episode with Micheal I. Jordan.
Radical AI⌗
Another weekly interview based podcast, but specifically focused on ethics in AI. I’ve only listened to a couple, but the quality of discussion is very high. I’d recommend the episode with Anima Anandkumar as a starter.
Learning Bayesian Statistics⌗
A podcast just about Bayesian statistics. Again only listened to a few of these so I can’t make any big claims, but the episodes I did listen to were quite technical, which I liked a lot.
Gradient Dissent⌗
This one is all about applying ML/AI in practice, and all the challenges that come with that. Only recently started it, but can recommend the episode with Zack Lipton, really interesting stuff!
Data Skeptic⌗
I listened to one episode about ML Ops, it was pretty good. Haven’t got round to any others yet, but the titles look interesting, so I’m including it here for good measure.
-
They are such great hosts that I am happy to listen to hours of R content, despite the fact I have never written a line of it in my life and don’t intend to. ↩︎