Machine learning
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Machine learning (ML) is a
ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.[4][5] When applied to business problems, it is known under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field's methods.
The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods. Data mining is a related (parallel) field of study, focusing on exploratory data analysis (EDA) through unsupervised learning.[7][8]
From a theoretical viewpoint, probably approximately correct (PAC) learning provides a framework for describing machine learning.
History
The term machine learning was coined in 1959 by
Although the earliest machine learning model was introduced in the 1950s when
By the early 1960s an experimental "learning machine" with
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."[19] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".[20]
Modern-day machine learning has two objectives. One is to classify data based on models which have been developed; the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions.[21]
Relationships to other fields
Artificial intelligence
As a scientific endeavor, machine learning grew out of the quest for
However, an increasing emphasis on the
Machine learning (ML), reorganized and recognized as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory.[25]
Data compression
There is a close connection between machine learning and compression. A system that predicts the
An alternative view can show compression algorithms implicitly map strings into implicit
According to AIXI theory, a connection more directly explained in Hutter Prize, the best possible compression of x is the smallest possible software that generates x. For example, in that model, a zip file's compressed size includes both the zip file and the unzipping software, since you can not unzip it without both, but there may be an even smaller combined form.
Examples of AI-powered audio/video compression software include
In
Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the
Data mining
Machine learning and
Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples).[35]
Generalization
The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms.
Statistics
Machine learning and
Conventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on previous experience are included for analysis. In contrast, machine learning is not built on a pre-structured model; rather, the data shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be.[38]
Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model,[39] wherein "algorithmic model" means more or less the machine learning algorithms like Random Forest.
Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.[40]
Statistical physics
Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyze the weight space of
Theory
A core objective of a learner is to generalize from its experience.[6][43] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.
The computational analysis of machine learning algorithms and their performance is a branch of
For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer.[44]
In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.
Approaches
Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system:
- Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
- Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
- driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.[6]
Although each algorithm has advantages and limitations, no single algorithm works for all problems.[45][46][47]
Supervised learning
Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.
Types of supervised-learning algorithms include active learning, classification and regression.[50] Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.
Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.
Unsupervised learning
Unsupervised learning algorithms find structures in data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Central applications of unsupervised machine learning include clustering, dimensionality reduction,[8] and density estimation.[51] Unsupervised learning algorithms also streamlined the process of identifying large indel based haplotypes of a gene of interest from pan-genome.[52]
Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity.
Semi-supervised learning
Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.
In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.[53]
Reinforcement learning
Reinforcement learning 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. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In reinforcement learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcements learning algorithms use dynamic programming techniques.[54] Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.
Dimensionality reduction
Other types
Other approaches have been developed which do not fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system. For example,
Self-learning
Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array (CAA).[58] It is learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.[59] The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine:
- in situation s perform action a
- receive a consequence situation s'
- compute emotion of being in the consequence situation v(s')
- update crossbar memory w'(a,s) = w(a,s) + v(s')
It is a system with only one input, situation, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations.[60]
Feature learning
Several learning algorithms aim at discovering better representations of the inputs provided during training.[61] Classic examples include principal component analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.
Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include
Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.
Sparse dictionary learning
Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of
Anomaly detection
In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.[70] Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions.[71]
In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object. Many outlier detection methods (in particular, unsupervised algorithms) will fail on such data unless aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.[72]
Three broad categories of anomaly detection techniques exist.[73] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit the least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model.
Robot learning
Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning,[74][75] and finally meta-learning (e.g. MAML).
Association rules
Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[76]
Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[77] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.
Based on the concept of strong rules,
Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[79]
Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[80][81][82] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[83] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set.
Models
A machine learning model is a type of mathematical model which, after being "trained" on a given dataset, can be used to make predictions or classifications on new data. During training, a learning algorithm iteratively adjusts the model's internal parameters to minimize errors in its predictions.[84] By extension the term model can refer to several level of specifity, from a general class of models and their associated learning algorithms, to a fully trained model with all its internal parameters tuned.[85]
Various types of models have been used and researched for machine learning systems, picking the best model for a task is called model selection.
Artificial neural networks
Artificial neural networks (ANNs), or
An ANN is a model based on a collection of connected units or nodes called "
The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.
Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[86]
Decision trees
Decision tree learning uses a
Support-vector machines
Support-vector machines (SVMs), also known as support-vector networks, are a set of related
Regression analysis
Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is
Bayesian networks
A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic
Gaussian processes
A Gaussian process is a stochastic process in which every finite collection of the random variables in the process has a multivariate normal distribution, and it relies on a pre-defined covariance function, or kernel, that models how pairs of points relate to each other depending on their locations.
Given a set of observed points, or input–output examples, the distribution of the (unobserved) output of a new point as function of its input data can be directly computed by looking like the observed points and the covariances between those points and the new, unobserved point.
Gaussian processes are popular surrogate models in Bayesian optimization used to do hyperparameter optimization.
Genetic algorithms
A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[90][91] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[92]
Belief functions
The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as
Training models
Typically, machine learning models require a high quantity of reliable data in order for the models to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative
Federated learning
Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[93]
Applications
There are many applications for machine learning, including:
- Agriculture
- Anatomy
- Adaptive website
- Affective computing
- Astronomy
- Automated decision-making
- Banking
- Behaviorism
- Bioinformatics
- Brain–machine interfaces
- Cheminformatics
- Citizen Science
- Climate Science
- Computer networks
- Computer vision
- Credit-card frauddetection
- Data quality
- DNA sequenceclassification
- Economics
- Financial market analysis[94]
- General game playing
- Handwriting recognition
- Healthcare
- Information retrieval
- Insurance
- Internet fraud detection
- Knowledge graph embedding
- Linguistics
- Machine learning control
- Machine perception
- Machine translation
- Marketing
- Medical diagnosis
- Natural language processing
- Natural language understanding
- Online advertising
- Optimization
- Recommender systems
- Robot locomotion
- Search engines
- Sentiment analysis
- Sequence mining
- Software engineering
- Speech recognition
- Structural health monitoring
- Syntactic pattern recognition
- Telecommunication
- Theorem proving
- Time-series forecasting
- Tomographic reconstruction[95]
- User behavior analytics
In 2006, the media-services provider
Recent advancements in machine learning have extended into the field of quantum chemistry, where novel algorithms now enable the prediction of solvent effects on chemical reactions, thereby offering new tools for chemists to tailor experimental conditions for optimal outcomes.[108]
Limitations
Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[109][110][111] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[112]
The "black box theory" poses another yet significant challenge. Black box refers to a situation where the algorithm or the process of producing an output is entirely opaque, meaning that even the coders of the algorithm cannot audit the pattern that the machine extracted out of the data.[113] The House of Lords Select Committee, which claimed that such an “intelligence system” that could have a “substantial impact on an individual’s life” would not be considered acceptable unless it provided “a full and satisfactory explanation for the decisions” it makes.[113]
In 2018, a self-driving car from
Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves.[118]
Bias
This section may require copy editing. (April 2024) |
Machine learning approaches in particular can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on human-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society.[119]
Language models learned from data have been shown to contain human-like biases.[120][121] In an experiment carried out by ProPublica, an investigative journalism organization, a machine learning algorithm's insight towards the recidivism rates among prisoners falsely flagged “black defendants high risk twice as often as white defendants.”[122] In 2015, Google photos would often tag black people as gorillas,[122] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[123] Similar issues with recognizing non-white people have been found in many other systems.[124] In 2016, Microsoft tested Tay, a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[125]
Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[126] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There's nothing artificial about AI...It's inspired by people, it's created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility."[127]
Explainability
Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI.[128] It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision.[129] By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively. XAI may be an implementation of the social right to explanation.
Overfitting
Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data but penalizing the theory in accordance with how complex the theory is.[130]
Other limitations and vulnerabilities
Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.[131] A real-world example is that, unlike humans, current image classifiers often do not primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies.[132][133]
Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. For some systems, it is possible to change the output by only changing a single adversarially chosen pixel.[134] Machine learning models are often vulnerable to manipulation and/or evasion via adversarial machine learning.[135]
Researchers have demonstrated how backdoors can be placed undetectably into classifying (e.g., for categories "spam" and well-visible "not spam" of posts) machine learning models which are often developed and/or trained by third parties. Parties can change the classification of any input, including in cases for which a type of data/software transparency is provided, possibly including white-box access.[136][137][138]
Model assessments
Classification of machine learning models can be validated by accuracy estimation techniques like the
In addition to overall accuracy, investigators frequently report
Ethics
Machine learning poses a host of ethical questions. Systems that are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[141] For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a computer program trained from data of previous admissions staff and this program had denied nearly 60 candidates who were found to be either women or had non-European sounding names.[119] Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants.[142][143] Another example includes predictive policing company Geolitica's predictive algorithm that resulted in “disproportionately high levels of over-policing in low-income and minority communities” after being trained with historical crime data.[122]
While responsible collection of data and documentation of algorithmic rules used by a system is considered a critical part of machine learning, some researchers blame lack of participation and representation of minority population in the field of AI for machine learning's vulnerability to biases.[144] In fact, according to research carried out by the Computing Research Association (CRA) in 2021, “female faculty merely make up 16.1%” of all faculty members who focus on AI among several universities around the world.[145] Furthermore, among the group of “new U.S. resident AI PhD graduates,” 45% identified as white, 22.4% as Asian, 3.2% as Hispanic, and 2.4% as African American, which further demonstrates a lack of diversity in the field of AI.[145]
AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on objectivity and logical reasoning.[146] Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[147][148]
Other forms of ethical challenges, not related to personal biases, are seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines.[149] This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated.[150]
Hardware
Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training
Neuromorphic/Physical Neural Networks
A
Embedded Machine Learning
Embedded Machine Learning is a sub-field of machine learning, where the machine learning model is run on
Software
Software suites containing a variety of machine learning algorithms include the following:
Free and open-source software
- Caffe
- Deeplearning4j
- DeepSpeed
- ELKI
- Google JAX
- Infer.NET
- Keras
- Kubeflow
- LightGBM
- Mahout
- Mallet
- Microsoft Cognitive Toolkit
- ML.NET
- mlpack
- MXNet
- OpenNN
- Orange
- pandas (software)
- ROOT (TMVA with ROOT)
- scikit-learn
- Shogun
- Spark MLlib
- SystemML
- TensorFlow
- Torch / PyTorch
- MOA
- XGBoost
- Yooreeka
Proprietary software with free and open-source editions
Proprietary software
- Amazon Machine Learning
- Angoss KnowledgeSTUDIO
- Azure Machine Learning
- IBM Watson Studio
- Google Cloud Vertex AI
- Google Prediction API
- IBM SPSS Modeler
- KXEN Modeler
- LIONsolver
- Mathematica
- MATLAB
- Neural Designer
- NeuroSolutions
- Oracle Data Mining
- Oracle AI Platform Cloud Service
- PolyAnalyst
- RCASE
- SAS Enterprise Miner
- SequenceL
- Splunk
- STATISTICAData Miner
Journals
- Journal of Machine Learning Research
- Machine Learning
- Nature Machine Intelligence
- Neural Computation
- IEEE Transactions on Pattern Analysis and Machine Intelligence
Conferences
- AAAI Conference on Artificial Intelligence
- Association for Computational Linguistics (ACL)
- European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
- International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)
- International Conference on Machine Learning (ICML)
- International Conference on Learning Representations (ICLR)
- International Conference on Intelligent Robots and Systems (IROS)
- Conference on Knowledge Discovery and Data Mining (KDD)
- Conference on Neural Information Processing Systems (NeurIPS)
See also
- Automated machine learning – Process of automating the application of machine learning
- Big data – Extremely large or complex datasets
- Differentiable programming – Programming paradigm
- Force control
- List of important publications in machine learning
- List of datasets for machine-learning research
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Further reading
- Nils J. Nilsson, Introduction to Machine Learning Archived 2019-08-16 at the Wayback Machine.
- ISBN 0-387-95284-5.
- ISBN 978-0-465-06570-7
- Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664pp., ISBN 978-0-12-374856-0.
- Ethem Alpaydin (2004). Introduction to Machine Learning, MIT Press, ISBN 978-0-262-01243-0.
- ISBN 0-521-64298-1
- ISBN 0-471-05669-3.
- ISBN 0-19-853864-2.
- Stuart Russell & Peter Norvig, (2009). Artificial Intelligence – A Modern Approach Archived 2011-02-28 at the ISBN 9789332543515.
- Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56–62, 1957.
- Ray Solomonoff, An Inductive Inference Machine Archived 2011-04-26 at the Wayback Machine A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI.
- Kevin P. Murphy (2021). Probabilistic Machine Learning: An Introduction Archived 2021-04-11 at the Wayback Machine, MIT Press.
External links
- Quotations related to Machine learning at Wikiquote
- International Machine Learning Society
- mloss is an academic database of open-source machine learning software.