... [Here are] some common intuitions:
- In supervised learning, the particular algorithm is usually less important than engineering and tuning it really well. In unsupervised learning, we’d think carefully about the structure of the data and build a model which reflects that structure.
- In supervised learning, except in small-data settings, we throw whatever features we can think of at the problem. In unsupervised learning, we carefully pick the features we think best represent the aspects of the data we care about.
- Supervised learning seems to have many algorithms with strong theoretical guarantees, and unsupervised learning very few.
- Off-the-shelf algorithms perform very well on a wide variety of supervised tasks, but unsupervised learning requires more care and expertise to come up with an appropriate model.
I’d argue that this is deceptive. I think real division in machine learning isn’t between supervised and unsupervised, but what I’ll term predictive learning and representation learning. I haven’t heard it described in precisely this way before, but I think this distinction reflects a lot of our intuitions about how to approach a given machine learning problem.