>What are the "other learning algorithms" ? SVMs, AdaBoost & co, random forests - just to name a few popular ones. >Neural nets are universal function approxi

>What are the "other learning algorithms" ? SVMs, AdaBoost & co, random forests - just to name a few popular ones. >Neural nets are universal function approximators. There isnt something they can't learn. Which types of functions you can approximate has nothing to do with being able to "learn everything". Simple neural nets are a great illustration of this point. Networks that can theoretically approximate the target function can easily overfit or oscillate if not set up properly. In other words, the ability to approximate is the property of your model, while the ability to learn things is the property of your training algorithm.