> Gradient descents are a special case of associative learning rules assuming all data points the same importance. No. Gradient descent is an optimization meth

> Gradient descents are a special case of associative learning rules assuming all data points the same importance. No. Gradient descent is an optimization method that has nothing to do with learning. It is used to optimize parameterized functions that are said to be "learning", but it's not the only approach. It is also trivially easy, and not uncommon, to use different weights for different data points. Can you clarify what you mean with associative learning?