Outline of machine learning

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The following outline is provided as an overview of and topical guide to machine learning:

Contents

Machine learning subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [2] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. [3] Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.

What type of thing is machine learning?

Branches of machine learning

Subfields of machine learning

Cross-disciplinary fields involving machine learning

Applications of machine learning

Machine learning hardware

Machine learning tools

Machine learning frameworks

Proprietary machine learning frameworks

Open source machine learning frameworks

Machine learning libraries

Machine learning algorithms

Machine learning methods

Instance-based algorithm

Regression analysis

Dimensionality reduction

Dimensionality reduction

Ensemble learning

Ensemble learning

Meta-learning

Meta-learning

Reinforcement learning

Reinforcement learning

Supervised learning

Supervised learning

Bayesian

Bayesian statistics

Decision tree algorithms

Decision tree algorithm

Linear classifier

Linear classifier

Unsupervised learning

Unsupervised learning

Artificial neural networks

Artificial neural network

Association rule learning

Association rule learning

Hierarchical clustering

Hierarchical clustering

Cluster analysis

Cluster analysis

Anomaly detection

Anomaly detection

Semi-supervised learning

Semi-supervised learning

Deep learning

Deep learning

Other machine learning methods and problems

Machine learning research

History of machine learning

History of machine learning

Machine learning projects

Machine learning projects

Machine learning organizations

Machine learning organizations

Machine learning conferences and workshops

Machine learning publications

Books on machine learning

Machine learning journals

Persons influential in machine learning

See also

Other


Further reading

Related Research Articles

<span class="mw-page-title-main">Supervised learning</span> A paradigm in machine learning

Supervised learning (SL) is a paradigm in machine learning where input objects and a desired output value train a model. The training data is processed, building a function that maps new data on expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way. This statistical quality of an algorithm is measured through the so-called generalization error.

Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent pattern. PR has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to surpass many previous approaches in performance.

Unsupervised learning is a method in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it.

<span class="mw-page-title-main">Time series</span> Sequence of data points over time

In mathematics, a time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.

In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient.

<span class="mw-page-title-main">Transfer learning</span> Machine learning technique

Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks. This topic is related to the psychological literature on transfer of learning, although practical ties between the two fields are limited. Reusing/transferring information from previously learned tasks to new tasks has the potential to significantly improve learning efficiency.

The following outline is provided as an overview of and topical guide to artificial intelligence:

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.

Structured prediction or structured (output) learning is an umbrella term for supervised machine learning techniques that involves predicting structured objects, rather than scalar discrete or real values.

There are many types of artificial neural networks (ANN).

mlpy is a Python, open-source, machine learning library built on top of NumPy/SciPy, the GNU Scientific Library and it makes an extensive use of the Cython language. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3.

mlpack

mlpack is a machine learning software library for C++, built on top of the Armadillo library and the ensmallen numerical optimization library. mlpack has an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. Its intended target users are scientists and engineers.

<span class="mw-page-title-main">JASP</span> Free and open-source statistical program

JASP is a free and open-source program for statistical analysis supported by the University of Amsterdam. It is designed to be easy to use, and familiar to users of SPSS. It offers standard analysis procedures in both their classical and Bayesian form. JASP generally produces APA style results tables and plots to ease publication. It promotes open science via integration with the Open Science Framework and reproducibility by integrating the analysis settings into the results. The development of JASP is financially supported by several universities and research funds. As the JASP GUI is developed in C++ using Qt framework, some of the team left to make a notable fork which is Jamovi which has its GUI developed in JavaScript and HTML5.

This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.

Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining.

References

  1. http://www.britannica.com/EBchecked/topic/1116194/machine-learning  This tertiary source reuses information from other sources but does not name them.
  2. Phil Simon (March 18, 2013). Too Big to Ignore: The Business Case for Big Data. Wiley. p. 89. ISBN   978-1-118-63817-0.
  3. Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning . 30: 271–274. doi: 10.1023/A:1007411609915 .
  4. "ACL - Association for Computational Learning".
  5. Settles, Burr (2010), "Active Learning Literature Survey" (PDF), Computer Sciences Technical Report 1648. University of Wisconsin–Madison, retrieved 2014-11-18
  6. Rubens, Neil; Elahi, Mehdi; Sugiyama, Masashi; Kaplan, Dain (2016). "Active Learning in Recommender Systems". In Ricci, Francesco; Rokach, Lior; Shapira, Bracha (eds.). Recommender Systems Handbook (2 ed.). Springer US. doi:10.1007/978-1-4899-7637-6. hdl:11311/1006123. ISBN   978-1-4899-7637-6. S2CID   11569603.