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Machine learning

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.[1] Recently, artificial neural networks have been able to surpass many previous approaches in performance.[2][3]

For the journal, see Machine Learning (journal).

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.

: 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.

Supervised 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).

Unsupervised learning

: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as 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]

Reinforcement learning

Model assessments

Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[139]


In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[140]

KNIME

RapidMiner

Journal of Machine Learning Research

Machine Learning

Nature Machine Intelligence

Neural Computation

IEEE Transactions on Pattern Analysis and Machine Intelligence

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)

 – Process of automating the application of machine learning

Automated machine learning

 – Extremely large or complex datasets

Big data

 – Programming paradigm

Differentiable programming

Force control

List of important publications in machine learning

List of datasets for machine-learning research

(September 22, 2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. ISBN 978-0465065707.

Domingos, Pedro

(1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. Archived from the original on 26 July 2020. Retrieved 18 November 2019.

Nilsson, Nils

; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2.

Russell, Stuart J.

Poole, David; ; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. ISBN 978-0-19-510270-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020.

Mackworth, Alan

Quotations related to Machine learning at Wikiquote

International Machine Learning Society

is an academic database of open-source machine learning software.

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