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* [[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]]).
* [[Reinforcement learning]]: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as [[Autonomous car|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 maximise.<ref name="bishop2006"/>
Although each algorithm has advantages and limitations, no single algorithm works for all problems.<ref>{{cite journal |last1=Jordan |first1=M. I. |last2=Mitchell |first2=T. M. |title=Machine learning: Trends, perspectives, and prospects |journal=Science |date=17 July 2015 |volume=349 |issue=6245 |pages=255–260 |doi=10.1126/science.aaa8415|pmid=26185243 |bibcode=2015Sci...349..255J |s2cid=677218 }}</ref><ref>{{cite book |last1=El Naqa |first1=Issam |last2=Murphy |first2=Martin J. |title=Machine Learning in Radiation Oncology |chapter=What is Machine Learning? |date=2015 |pages=3–11 |doi=10.1007/978-3-319-18305-3_1|isbn=978-3-319-18304-6 |s2cid=178586107 }}</ref><ref>{{cite journal |last1=Okolie |first1=Jude A. |last2=Savage |first2=Shauna |last3=Ogbaga |first3=Chukwuma C. |last4=Gunes |first4=Burcu |title=Assessing the potential of machine learning methods to study the removal of pharmaceuticals from wastewater using biochar or activated carbon |journal=Total Environment Research Themes |date=June 2022 |volume=1–2 |
=== Supervised learning ===
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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 [[linear regression]], where a single line is drawn to best fit the given data according to a mathematical criterion such as [[ordinary least squares]]. The latter is often extended by [[regularization (mathematics)|regularisation]] methods to mitigate overfitting and bias, as in [[ridge regression]]. When dealing with non-linear problems, go-to models include [[polynomial regression]] (for example, used for trendline fitting in Microsoft Excel<ref>{{cite web|last1=Stevenson|first1=Christopher|title=Tutorial: Polynomial Regression in Excel|url=https://facultystaff.richmond.edu/~cstevens/301/Excel4.html|website=facultystaff.richmond.edu|access-date=22 January 2017|archive-date=2 June 2013|archive-url=https://web.archive.org/web/20130602200850/https://facultystaff.richmond.edu/~cstevens/301/Excel4.html|url-status=live}}</ref>), [[logistic regression]] (often used in [[statistical classification]]) or even [[kernel regression]], which introduces non-linearity by taking advantage of the [[kernel trick]] to implicitly map input variables to higher-dimensional space.
[[General linear model|Multivariate linear regression]] extends the concept of linear regression to handle multiple dependent variables simultaneously. This approach estimates the relationships between a set of input variables and several output variables by fitting a [[Multidimensional system|multidimensional]] linear model. It is particularly useful in scenarios where outputs are interdependent or share underlying patterns, such as predicting multiple economic indicators or reconstructing images,<ref>{{cite journal |last1= Wanta |first1= Damian |last2= Smolik |first2= Aleksander |last3= Smolik |first3= Waldemar T. |last4= Midura |first4= Mateusz |last5= Wróblewski |first5= Przemysław |date= 2025 |title= Image reconstruction using machine-learned pseudoinverse in electrical capacitance tomography |journal= Engineering Applications of Artificial Intelligence |volume= 142|
=== Bayesian networks ===
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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.<ref>{{Cite journal |last1=Chung |first1=Yunsie |last2=Green |first2=William H. |date=2024 |title=Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates |journal=Chemical Science |language=en |volume=15 |issue=7 |pages=2410–2424 |doi=10.1039/D3SC05353A |issn=2041-6520 |pmc=10866337 |pmid=38362410 }}</ref>
Machine Learning is becoming a useful tool to investigate and predict evacuation decision making in large scale and small scale disasters. Different solutions have been tested to predict if and when householders decide to evacuate during wildfires and hurricanes.<ref>{{Cite journal |last1=Sun |first1=Yuran |last2=Huang |first2=Shih-Kai |last3=Zhao |first3=Xilei |date=1 February 2024 |title=Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods |journal=International Journal of Disaster Risk Science |language=en |volume=15 |issue=1 |pages=134–148 |doi=10.1007/s13753-024-00541-1 |issn=2192-6395 |doi-access=free |arxiv=2303.06557 |bibcode=2024IJDRS..15..134S }}</ref><ref>{{Citation |last1=Sun |first1=Yuran |title=8 - AI for large-scale evacuation modeling: promises and challenges |date=1 January 2024 |work=Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure |pages=185–204 |editor-last=Naser |editor-first=M. Z. |url=https://www.sciencedirect.com/science/article/pii/B9780128240731000149 |access-date=19 May 2024 |series=Woodhead Publishing Series in Civil and Structural Engineering |publisher=Woodhead Publishing |isbn=978-0-12-824073-1 |last2=Zhao |first2=Xilei |last3=Lovreglio |first3=Ruggiero |last4=Kuligowski |first4=Erica |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519121547/https://www.sciencedirect.com/science/article/abs/pii/B9780128240731000149 |url-status=live }}</ref><ref>{{Cite journal |last1=Xu |first1=Ningzhe |last2=Lovreglio |first2=Ruggiero |last3=Kuligowski |first3=Erica D. |last4=Cova |first4=Thomas J. |last5=Nilsson |first5=Daniel |last6=Zhao |first6=Xilei |date=1 March 2023 |title=Predicting and Assessing Wildfire Evacuation Decision-Making Using Machine Learning: Findings from the 2019 Kincade Fire |url=https://doi.org/10.1007/s10694-023-01363-1 |journal=Fire Technology |language=en |volume=59 |issue=2 |pages=793–825 |doi=10.1007/s10694-023-01363-1 |issn=1572-8099 |access-date=19 May 2024 |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519121534/https://link.springer.com/article/10.1007/s10694-023-01363-1 |url-status=live |url-access=subscription }}</ref> Other applications have been focusing on pre evacuation decisions in building fires.<ref>{{Cite journal |last1=Wang |first1=Ke |last2=Shi |first2=Xiupeng |last3=Goh |first3=Algena Pei Xuan |last4=Qian |first4=Shunzhi |date=1 June 2019 |title=A machine learning based study on pedestrian movement dynamics under emergency evacuation |url=https://www.sciencedirect.com/science/article/pii/S037971121830376X |journal=Fire Safety Journal |volume=106 |pages=163–176 |doi=10.1016/j.firesaf.2019.04.008 |bibcode=2019FirSJ.106..163W |issn=0379-7112 |access-date=19 May 2024 |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519121539/https://www.sciencedirect.com/science/article/abs/pii/S037971121830376X |url-status=live |hdl=10356/143390 |hdl-access=free }}</ref><ref>{{Cite journal |last1=Zhao |first1=Xilei |last2=Lovreglio |first2=Ruggiero |last3=Nilsson |first3=Daniel |date=1 May 2020 |title=Modelling and interpreting pre-evacuation decision-making using machine learning |url=https://www.sciencedirect.com/science/article/pii/S0926580519313184 |journal=Automation in Construction |volume=113 |
Machine learning is also emerging as a promising tool in geotechnical engineering, where it is used to support tasks such as ground classification, hazard prediction, and site characterization. Recent research emphasizes a move toward data-centric methods in this field, where machine learning is not a replacement for engineering judgment, but a way to enhance it using site-specific data and patterns.<ref>{{Cite journal |last1=Phoon |first1=Kok-Kwang |last2=Zhang |first2=Wengang |date=2023-01-02 |title=Future of machine learning in geotechnics |url=https://www.tandfonline.com/doi/full/10.1080/17499518.2022.2087884 |journal=Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards |language=en |volume=17 |issue=1 |pages=7–22 |doi=10.1080/17499518.2022.2087884 |bibcode=2023GAMRE..17....7P |issn=1749-9518}}</ref>
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{{Main|Explainable artificial intelligence}}
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.<ref>{{cite journal |last1=Rudin |first1=Cynthia |title=Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead |journal=Nature Machine Intelligence |date=2019 |volume=1 |issue=5 |pages=206–215 |doi=10.1038/s42256-019-0048-x |pmid=35603010 |pmc=9122117 }}</ref> It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision.<ref>{{cite journal |last1=Hu |first1=Tongxi |last2=Zhang |first2=Xuesong |last3=Bohrer |first3=Gil |last4=Liu |first4=Yanlan |last5=Zhou |first5=Yuyu |last6=Martin |first6=Jay |last7=LI |first7=Yang |last8=Zhao |first8=Kaiguang |title=Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield|journal=Agricultural and Forest Meteorology |date=2023 |volume=336 |
=== Overfitting ===
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| doi = 10.1109/JIOT.2023.3340858
| url = https://research-portal.uws.ac.uk/en/publications/c8edfe21-77d0-4c3e-a8bc-d384faf605a0
}}</ref> Running models directly on these devices eliminates the need to transfer and store data on cloud servers for further processing, thereby reducing the risk of data breaches, privacy leaks and theft of intellectual property, personal data and business secrets. Embedded machine learning can be achieved through various techniques, such as [[hardware acceleration]],<ref>{{Cite book|last1=Giri|first1=Davide|last2=Chiu|first2=Kuan-Lin|last3=Di Guglielmo|first3=Giuseppe|last4=Mantovani|first4=Paolo|last5=Carloni|first5=Luca P.|title=2020 Design, Automation & Test in Europe Conference & Exhibition (DATE) |chapter=ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning |date=15 June 2020|chapter-url=https://ieeexplore.ieee.org/document/9116317|pages=1049–1054|doi=10.23919/DATE48585.2020.9116317|arxiv=2004.03640|isbn=978-3-9819263-4-7|s2cid=210928161|access-date=17 January 2022|archive-date=18 January 2022|archive-url=https://web.archive.org/web/20220118182342/https://ieeexplore.ieee.org/abstract/document/9116317?casa_token=5I_Tmgrrbu4AAAAA:v7pDHPEWlRuo2Vk3pU06194PO0-W21UOdyZqADrZxrRdPBZDMLwQrjJSAHUhHtzJmLu_VdgW|url-status=live}}</ref><ref>{{Cite web|last1=Louis|first1=Marcia Sahaya|last2=Azad|first2=Zahra|last3=Delshadtehrani|first3=Leila|last4=Gupta|first4=Suyog|last5=Warden|first5=Pete|last6=Reddi|first6=Vijay Janapa|last7=Joshi|first7=Ajay|date=2019|title=Towards Deep Learning using TensorFlow Lite on RISC-V|url=https://edge.seas.harvard.edu/publications/towards-deep-learning-using-tensorflow-lite-risc-v|access-date=17 January 2022|website=[[Harvard University]]|archive-date=17 January 2022|archive-url=https://web.archive.org/web/20220117031909/https://edge.seas.harvard.edu/publications/towards-deep-learning-using-tensorflow-lite-risc-v|url-status=live}}</ref> [[approximate computing]],<ref>{{Cite book|last1=Ibrahim|first1=Ali|last2=Osta|first2=Mario|last3=Alameh|first3=Mohamad|last4=Saleh|first4=Moustafa|last5=Chible|first5=Hussein|last6=Valle|first6=Maurizio|title=2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS) |chapter=Approximate Computing Methods for Embedded Machine Learning |date=21 January 2019
== Software ==
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