Regardless the impact machine learning techniques are having on the field, I still think is valuable to know and understand what are the classic methods that they are allegedly replacing.
Mostly what's changed is that what used to be heuristics and hand-crafted features is now getting replaced by proper learned models. Also some generative models are getting replaced discriminative models since deep learning does a very good job in creating those.
- considered obsolete (e.g. complicated and nasty, and still outclassed by NNs),
- building blocks of current techniques,
- provided inspiration for current techniques,
- are still state of the art.