Machine learning

machine-learninglearningstatistical learninglearning algorithmslearning algorithmmachine-learnedlearnmachine learning algorithmMLApplications of machine learning
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead.wikipedia
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Artificial intelligence

AIA.I.artificially intelligent
It is seen as a subset of artificial intelligence.
The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.

Data mining

data-miningdataminingknowledge discovery in databases
Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Predictive analytics

predictiveCARTpredictive analysis
In its application across business problems, machine learning is also referred to as predictive analytics.
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.

Supervised learning

supervisedsupervised machine learningsupervised classification
In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs.
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.

Statistical classification

Classification algorithms and regression algorithms are types of supervised learning.
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

Semi-supervised learning

semi-supervisedSemisupervised learningco-training
Semi-supervised learning algorithms develop mathematical models from incomplete training data, where a portion of the sample input doesn't have labels.
Semi-supervised learning is a class of machine learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.

Arthur Samuel

Arthur L. SamuelSamuel
The name machine learning was coined in 1959 by Arthur Samuel.
He coined the term "machine learning" in 1959.

Mathematical optimization

optimizationmathematical programmingoptimal
The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.
In Machine Learning, it is always necessary to continuously evaluate the quality of a data model by using a cost function where a minimum implies a set of possibly optimal parameters with an optimal (lowest) error.

Feature learning

representation learningefficient codingsefficient data codings
Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning.
In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data.

Cluster analysis

clusteringdata clusteringcluster
Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points.
It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.

Tom M. Mitchell

Tom Mitchell
Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."
Mitchell is known for his contributions to the advancement of machine learning, artificial intelligence, and cognitive neuroscience and is the author of the textbook Machine Learning.

Reinforcement learning

reward functionInverse reinforcement learningreinforcement
Reinforcement learning algorithms are given feedback in the form of positive or negative reinforcement in a dynamic environment, and are used in autonomous vehicles or in learning to play a game against a human opponent.
Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

Dimensionality reduction

dimension reductionreduce the dimensionalitydimensional reduction
Dimensionality reduction is the process of reducing the number of "features", or inputs, in a set of data.
In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.

Feature (machine learning)

feature vectorfeature spacefeatures
Dimensionality reduction is the process of reducing the number of "features", or inputs, in a set of data.
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed.

Topic model

topic modelingtopic modelling
Other specialized algorithms in machine learning include topic modeling, where the computer program is given a set of natural language documents and finds other documents that cover similar topics.
In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents.

Meta learning (computer science)

meta learningmeta-learningmetalearning
Meta learning algorithms learn their own inductive bias based on previous experience.
is a subfield of machine learning where automatic learning algorithms are applied on metadata about machine learning experiments.

Regression analysis

regressionmultiple regressionregression model
Classification algorithms and regression algorithms are types of supervised learning.
First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning.

Robot learning

learnlearn or gain new knowledgelearning robotic systems
In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans.
Robot learning is a research field at the intersection of machine learning and robotics.


Perceptronsperceptron algorithmFeedforward Neural Network (Perceptron)
They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers.

Unsupervised learning

unsupervisedunsupervised classificationunsupervised machine learning
Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.
Cluster analysis is a branch of machine learning that groups the data that has not been labelled, classified or categorized.

Inductive bias

Learning bias
Meta learning algorithms learn their own inductive bias based on previous experience.
The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered.

Active learning (machine learning)

Active learningactive or query learning
Active learning algorithms access the desired outputs (training labels) for a limited set of inputs based on a budget, and optimize the choice of inputs for which it will acquire training labels.
Active learning is a special case of machine learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points.

Pattern recognition

pattern analysispattern detectionpatterns
Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.
Pattern recognition is closely related to artificial intelligence and machine learning, together with applications such as data mining and knowledge discovery in databases (KDD), and is often used interchangeably with these terms.


back-propagationback propagationbackpropagate
Their main success came in the mid-1980s with the reinvention of backpropagation.
In machine learning, specifically deep learning, backpropagation (backprop, BP) is an algorithm widely used in the training of feedforward neural networks for supervised learning; generalizations exist for other artificial neural networks (ANNs), and for functions generally.

Michael I. Jordan

JordanMichael JordanJordan, Michael I.
According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.
Michael Irwin Jordan is an American scientist, professor at the University of California, Berkeley and researcher in machine learning, statistics, and artificial intelligence.