Both good.

A bit easier to get into than neurips, icml, or iclr, and also seen as less prestigious. They're generally a good fit for less trendy ml topics.

I would submit to either depending on when the work was ready.

It exactly matches Mitchell's definition of ml though.

> A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

https://towardsdatascience.com/what-is-machine-learning-and-types-of-machine-learning-andrews-machine-learning-part-1-9cd9755bc647#:~:text=Tom%20Mitchell%20provides%20a%20more,simple%20example%20to%20understand%20better%20.

Localisation error decreases the more data you have.

Slam exactly fits the definition of ml.

The more data you give it, the better the map gets, and the better we expect localisation to be.

It has no generalisation at all, but it is learning something very specific about a particular environment.

A thermostat responds to the environment. It turns on the heating when it gets too cold.

and the ML definition is just repeating the formal definition by Mitchell:

> A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

https://towardsdatascience.com/what-is-machine-learning-and-types-of-machine-learning-andrews-machine-learning-part-1-9cd9755bc647#:~:text=Tom%20Mitchell%20provides%20a%20more,simple%20example%20to%20understand%20better%20.

Because some people make processed data/pretrained models available online as pickle files.

It'd be nice to be able to open them without having to worry about bad actors nuking my home directory.