In one Udemy course was mentioned that "dropout is unique to neural networks". However, I remember an example of decision trees where nodes that are not participating in the overall result are removed, and I think that this technique is also called "dropout". Am I correct?
I'm sure you can use dropout in any parameterized model, but I suspect it'll only really be helpful if you have enough parameters/nodes. Also dropout in neural nets has a Bayesian meaning, Yarin Gal for example has done lots of work on this.
In your decision tree example, I believe you're talking about pruning, which is different. In that context you're removing nodes that you know aren't contributing. In dropout, you randomly turn off nodes during training in order to prevent individual nodes from being too influential, but the nodes are never removed.
You might also be interested in L1 regularization in parameterized models. This is when you add a penalty according to the absolute weights (rather than square weights), which tends to drive less useful weights to 0. Then you can remove the nodes with almost no weight. This is more akin to your decision tree example rather than dropout though.