Hi there. On this page you will fine a number of short articles which discuss some of the lesser talked about issues with machine learning. These are rarely rigourous and do little to provide answers, they are intended to help those who are starting into the field of ML and are using resources freely available online.
Since I work in the field of assuring the safety of ML my bias will be to highlight those issues and assumptions which could, if applied without consideration, could lead to systems which give a false sense of security.
A second aim of these posts is to give me somewhere to document experiments and findings which have helped my understanding but are not suitable for publication in peer reviewed publications.Colin.
- But which model?
In traditional software engineering having access to source code allows you to build a model which is identical to that produced by the originating author. For machine learning however it's not quite that simple.