Data Science podcasts

I’m an avid podcast listener. Whenever there’s something that only requires sight or not much focus I’ll try to do it with my headphones on. Great thing about podcasts is that they they are more up-to-date than audiobooks and have reasonably short lengths, so there’s always a fit.

Wanting to be more current with machine learning topics I’ve found few podcasts. These are my recommendations:

Machine Learning competition on Seizure Prediction

tl;dr: Read subject and click on link below.

Some of you, i.e. those lucky ones with connection to the outside World, are probably aware about Machine Learning community trying to aggressively change our lives for better. Regardless whether we like it or not, they’re doing it. Some do this for money, others for fame, and those wicked ones just for fun.

Kaggle is a webpage that hosts Machine Learning competitions. They provide data (usually donated by companies or public organizations) and set a goal. These included detecting and classifying specific whales species from satellite images, or driving a remote car based on an hour of recording, or identifying patients that will return based on historic records, or … It’s actually pretty big. Some prices can be as big as $500,000.

Reason behind this email is one of Kaggel’s recent competitions — Predicting seizures in long-term human intracranial EEG recordings. The challenge is to “The challenge is to distinguish between ten minute long data clips covering an hour prior to a seizure, and ten minute iEEG clips of interictal activity.” Yes, many people has tried this and it’s ongoing research in many labs. The difference here is that you can actually see people’s attempts and their codes. You can read their discussions and follow their logics. It looks like an amazing source of information! Moreover, good contestants are really good at machine learning and they often do their work properly, i.e. complete the challenge.

This all is really important to me. As my background is much in EEG analysis and machine learning. The timing is a bit unfortunate, but I might give it a go.