Table 1.

The table describes the different types of ML, the mechanism on which they are based, some examples of possible applications, as well as some operating models for each type [13,14,16,17]. ANNs = artificial neural networks, DBSCAN = density-based spatial clustering of applications with noise, SARSA = state–action–reward–state–action.

Type of ML	Mechanism	Type of Data Provided	Problems that Can Solve	Examples of Models
Supervised learning (SL)	The algorithm is provided with tuples of input and output (x,y) and the algorithms infer the relation that maps the dataset	Labeled data	Classification task (discrete variable) Regression task (continuous variable)	Logistic regression Decision Tree Random Forest ANNs
Unsupervised learning (UL)	The algorithm exhibits self-organization to capture hidden patterns in data	Unlabeled data	Clustering Association Anomalies detection	Hierarchical clustering K-mean DBSCAN ANNs
Semi-supervised learning (SSL)	Falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data; a mix of SL and UL	Mostly unlabeled data, with a small amount of labeled data	Transductive task (infer the correct labels for the given unlabeled data) or inductive tasks (infer the correct mapping from x to y).	Generative model Self-training model Co-training model Transductive model Graph-based model
Reinforcement learning (RL)	The algorithm is programmed with a goal and a set of rules. It tends to a nearly optimal policy that maximizes the “reward function” or reinforcement signals	Not needing labeled input/output pairs to be presented, only a numerical performance score is given as guidance.	Economics and game theory under bounded rationality, control theory	Monte Carlo methods Q-learning SARSA methods