In supervised learning, a computer is trained to predict human-designated labels, such as type of tree-based labeled tree pictures; unsupervised learning is not dependent on labels. It's capable of making its own prediction workflow such as attempting to predict each successive word in a particular sentence. Reinforcement learning permits an agent to discover action sequences that optimize its total benefits, such as winning games and predicting elections, without explicit examples of good techniques, enabling autonomy.