Types of Machine Learning Algorithms

Machine learning algorithms are organized into taxonomy, based on the desired outcome of the algorithm. Common algorithm types include: • 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. The performance and computational analysis of machine learning algorithms is a branch of statistics known as computational learning theory. Machine learning is about designing algorithms that allow a computer to learn. Learning is not necessarily involves consciousness but learning is a matter of finding statistical regularities or other patterns in the data. Thus, many machine learning algorithms will barely resemble how human might approach a learning task. However, learning algorithms can give insight into the relative difficulty of learning in different environments