Learning Paradigms
Before picking an algorithm, answer one question: what does the training signal look like? A dataset of (input, correct-output) pairs calls for a different family of methods than a pile of unlabelled data, which in turn differs from a system that only gets a delayed reward for a sequence of actions. The paradigm is chosen by the data you have, not by which algorithm sounds most impressive.