Types of Machine Learning Algorithms

• Supervised learning --where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function. • Unsupervised learning --which models a set of inputs: labeled examples are not available. • Semi-supervised learning --which combines both labeled and unlabeled examples to generate an appropriate function or classifier. • Reinforcement learning --where the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm. • Transduction --similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and new inputs. • Learning to learn --where the algorithm learns its own inductive bias based on previous experience.