There are no convincing reasons to consider logistic regression to be outside of ML. ML isnt just deep learning. To me its a collection of mathematical tools that help in designing predictors. This involves stats, optimization, algorithms, stochastic processes, information theory.
The main difference between stats and ml is (i) community, (ii) stress on the details of compute and the focus on prediction accuracy rather than accurate recovery of parameters. A scheme that ensures good prediction even if the parameter is not recovered, for ML that's still a success.
The main difference between stats and ml is (i) community, (ii) stress on the details of compute and the focus on prediction accuracy rather than accurate recovery of parameters. A scheme that ensures good prediction even if the parameter is not recovered, for ML that's still a success.