Various techniques are employed in each of the machine learning methods. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labelled training data consisting of a set of training examples. Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no preexisting labels and with a minimum of human supervision. In contrast to supervised learning that usually makes use of human-labelled data, unsupervised learning, also known as self-organising allows for modelling of probability densities over inputs. Semi-supervised learning is an approach to machine learning that combines a small amount of labelled data with a large amount of unlabelled data during training.
| Supervised Learning | Unsupervised Learning | Semi-Supervised Learning | Reinforcement Learning | |
|---|---|---|---|---|
| Bayesian Statistics | Affinity Analysis | See Supervised Learning | Artificial Neural Networks (ANN) | |
| Decision Trees | Clustering | Markov Decision Process (MDP) | ||
| Forecasting | Clustering: K-Means | Q-Learning | ||
| Neural Networks | Nearest-Neighbour Mapping | |||
| Regression Analysis | Self-Organising Maps | |||
| Support Vector Machines | Singular Value Decomposition | |||
