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Machine learning tasks are grouped into several broad classifications.
In supervised learning, the algorithm makes a mathematical model of an assemblage of data that contains both the inputs and the desired outputs. Considering an example, if the task decides whether an image included a certain object, the training data for a supervised learning algorithm would insert images with and without that input (image) and each image would have an output (label) signifying whether it is included in the object. In exceptional cases, the input may be only partly available or limited to special feedback. Semi-supervised learning algorithms generate mathematical models from inadequate training data, where a portion of the sample input doesn't hold labels.
Regression and classification algorithms are types of supervised learning.
Classification algorithms are applied when the outputs are confined to a limited set of values. For a classification algorithm that refines emails, the input would be an incoming email, and the output would be the title of the folder in which to register the email. For an algorithm that recognises spam emails, the output would be the forecast of either "spam" or "not spam", interpreted by the Boolean values true and false.
Regression algorithms are noted for their continuous outputs, meaning they may have any value within a range. Samples of a continuous value are the temperature, price of an object or length.
In unsupervised learning, the algorithm forms a mathematical model from a kit of data which comprises only inputs and no desired output labels. Unsupervised learning algorithms are utilised to find structure in the data, like clustering or grouping of data points. Unsupervised learning can recognise patterns in the data and can group the inputs into sections, as in feature learning. Dimensionality reduction is the method of reducing the number of inputs or "features" in a set of data.
Active learning algorithms adjust the desired outputs for a limited set of budget based inputs and optimise the choice of inputs for which it will acquire training labels. When used interactively, those can be given to a human user for labelling.
In a dynamic environment, feedbacks are given in the form of positive or negative reinforcement in the case of Reinforcement learning algorithms and are used in self-sufficient vehicles or in learning to play a game upon a human opponent.
Machine learning includes other specialized algorithms like topic modelling, where the computer program is provided with a set of natural language documents and finds other documents that incorporate similar topics.
Machine learning algorithms can be used to attain the unobservable probability density function in density estimation problems. Learning their own inductive bias based on previous experience happens in Meta-learning algorithms. In developmental robotics, robot learning algorithms produce their own sequences of learning experiences, also understood as a curriculum, to cumulatively receive new skills through self-guided research and social interaction with humans. Those robots use guidance mechanisms such as motor synergies, active learning, imitation and maturation.
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