Opening the iTunes Store.If iTunes doesn’t open, click the iTunes icon in your Dock or on your Windows desktop.Progress Indicator
Opening Apple Books.If Apple Books doesn't open, click the Books app in your Dock.Progress Indicator

iTunes is the world's easiest way to organize and add to your digital media collection.

We are unable to find iTunes on your computer. To download and subscribe to Unsupervised by Inbar Naor & Shir Meir Lador, get iTunes now.

Do you already have iTunes? Click I Have iTunes to open it now.

I Have iTunes Free Download


By Inbar Naor & Shir Meir Lador

To listen to an audio podcast, mouse over the title and click Play. Open iTunes to download and subscribe to podcasts.


Unsupervised is a podcast about Data Science in Israel. At each episode we interview an industry professional or a researcher from academia and discuss different aspects and problems in data science. We want to give a peek to what’s going on with data science across the Israeli industry and also to talk about different algorithms, tools, papers, methods and pretty much everything that’s interesting and related to Data Science and Machine Learning. The podcast is aimed to data science professionals and researchers, as well as for those who work and collaborate with data science teams and beginners in the field. All Episodes are recorded in Hebrew. We want to thank Samsung Next for hosting us.

Customer Reviews

Great for Hebrew-speaking data scientists

The interviewers lead the discussions in the right direction while maintaining a good depth. The guests provide varying perspectives of data science (e.g., industry vs. academic, management, etc.). Highly recommended for Hebrew-speaking data scientists.

Great team, well crafted podcast

Really top notch stuff. As a data scientist myself I love listening to it, learning new things about my profession and the people in it!

Great podcast

Great to have a podcast abot the amazing data science ecosystem in Israel

Listeners also subscribed to

View in iTunes

Customer Ratings