Actually supervised learning is "learning missing data dimension" by parameter tuning via associative learning rules.

Gradient descents are a special case of associative learning rules assuming all data points the same importance.

A type of associative learning rules is Hebbian learning rule.

In the very fundamental we only need associative learning. Of course, for practical application we need diverse tools with different conceptual frameworks to choose for commercial: human resource or cost performance.