Supervised learning algorithms are “trained” using labeled examples where the desired output is known.
About 10 to 20 percent of machine learning is unsupervised learning, although this area is growing rapidly.
Unsupervised learning is a type of machine learning where the system operates on unlabeled examples. In this case, the system is not told the “right answer.”
The algorithm tries to find a hidden structure or manifold in unlabeled data.
Semi-supervised learning is a class of supervised learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.
Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data).
Reinforcement learning has three primary components: the agent (i.e., the learner or decision maker, the environment (i.e., everything the agent interacts with), and actions (i.e., what the agent can do).