Editing Machine learning (section)

== Applications ==
There are many applications for machine learning, including:
{{cols|colwidth=21em}}
* [[Precision agriculture|Agriculture]]
* [[Computational anatomy|Anatomy]]
* [[Adaptive website]]
* [[Affective computing]]
* [[Astroinformatics|Astronomy]]
* [[Automated decision-making]]
* [[Banking]]
* [[Behaviorism]]
* [[Bioinformatics]]
* [[Brain–computer interface|Brain–machine interfaces]]
* [[Cheminformatics]]
* [[Citizen Science]]
* [[Climate Science]]
* [[Network simulation|Computer networks]]
* [[Computer vision]]
* [[Credit-card fraud]] detection
* [[Data quality]]
* [[DNA sequence]] classification
* [[Computational economics|Economics]]
* [[Financial market]] analysis<ref>Machine learning is included in the [[Chartered Financial Analyst (CFA)#Curriculum|CFA Curriculum]] (discussion is top-down); see: [https://www.cfainstitute.org/-/media/documents/study-session/2020-l2-ss3.ashx Kathleen DeRose and Christophe Le Lanno (2020). "Machine Learning"] {{Webarchive|url=https://web.archive.org/web/20200113085425/https://www.cfainstitute.org/-/media/documents/study-session/2020-l2-ss3.ashx |date=13 January 2020 }}.</ref>
* [[General game playing]]
* [[Handwriting recognition]]
* [[Artificial intelligence in healthcare|Healthcare]]
* [[Information retrieval]]
* [[Insurance]]
* [[Internet fraud]] detection
* [[Knowledge graph embedding]]
* [[Computational linguistics|Linguistics]]
* [[Machine learning control]]
* [[Machine perception]]
* [[Machine translation]]
* [[Material Engineering]]
* [[Marketing]]
* [[Automated medical diagnosis|Medical diagnosis]]
* [[Natural language processing]]
* [[Natural-language understanding|Natural language understanding]]
* [[Online advertising]]
* [[Optimisation]]
* [[Recommender system]]s
* [[Robot locomotion]]
* [[Search engines]]
* [[Sentiment analysis]]
* [[Sequence mining]]
* [[Software engineering]]
* [[Speech recognition]]
* [[Structural health monitoring]]
* [[Syntactic pattern recognition]]
* [[Telecommunications]]
* [[Automated theorem proving|Theorem proving]]
* [[Time series|Time-series forecasting]]
* [[Tomographic reconstruction]]<ref>{{cite journal |last1= Ivanenko |first1= Mikhail |last2= Smolik |first2= Waldemar T. |last3= Wanta |first3= Damian |last4= Midura |first4= Mateusz |last5= Wróblewski |first5= Przemysław  |last6= Hou |first6= Xiaohan |last7= Yan |first7= Xiaoheng |date= 2023 |title= Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax  |journal= Sensors |volume= 23|issue= 18|page= 7774|doi= 10.3390/s23187774|pmid= 37765831 |pmc= 10538128 |bibcode= 2023Senso..23.7774I |doi-access= free}}</ref>
* [[User behaviour analytics]]
{{colend}}

In 2006, the media-services provider [[Netflix]] held the first "[[Netflix Prize]]" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from [[AT&T Labs]]-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an [[Ensemble Averaging|ensemble model]] to win the Grand Prize in 2009 for $1 million.<ref>[https://web.archive.org/web/20151110062742/http://www2.research.att.com/~volinsky/netflix/ "BelKor Home Page"] research.att.com</ref> Shortly after the prize was awarded, Netflix realised that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.<ref>{{cite web|url=http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html|title=The Netflix Tech Blog: Netflix Recommendations: Beyond the 5 stars (Part 1)|access-date=8 August 2015|date=6 April 2012|archive-url=https://web.archive.org/web/20160531002916/http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html|archive-date=31 May 2016}}</ref> In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.<ref>{{cite web|url=https://www.wsj.com/articles/SB10001424052748703834604575365310813948080|title=Letting the Machines Decide|author=Scott Patterson|date=13 July 2010|publisher=[[The Wall Street Journal]]|access-date=24 June 2018|archive-date=24 June 2018|archive-url=https://web.archive.org/web/20180624151019/https://www.wsj.com/articles/SB10001424052748703834604575365310813948080|url-status=live}}</ref> In 2012, co-founder of [[Sun Microsystems]], [[Vinod Khosla]], predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software.<ref>{{cite web|url=https://techcrunch.com/2012/01/10/doctors-or-algorithms/|author=Vinod Khosla|publisher=Tech Crunch|title=Do We Need Doctors or Algorithms?|date=10 January 2012|access-date=20 October 2016|archive-date=18 June 2018|archive-url=https://web.archive.org/web/20180618175811/https://techcrunch.com/2012/01/10/doctors-or-algorithms/|url-status=live}}</ref> In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognised influences among artists.<ref>[https://medium.com/the-physics-arxiv-blog/when-a-machine-learning-algorithm-studied-fine-art-paintings-it-saw-things-art-historians-had-never-b8e4e7bf7d3e When A Machine Learning Algorithm Studied Fine Art Paintings, It Saw Things Art Historians Had Never Noticed] {{Webarchive|url=https://web.archive.org/web/20160604072143/https://medium.com/the-physics-arxiv-blog/when-a-machine-learning-algorithm-studied-fine-art-paintings-it-saw-things-art-historians-had-never-b8e4e7bf7d3e |date=4 June 2016 }}, ''The Physics at [[ArXiv]] blog''</ref> In 2019 [[Springer Nature]] published the first research book created using machine learning.<ref>{{Cite web|url=https://www.theverge.com/2019/4/10/18304558/ai-writing-academic-research-book-springer-nature-artificial-intelligence|title=The first AI-generated textbook shows what robot writers are actually good at|last=Vincent|first=James|date=10 April 2019|website=The Verge|access-date=5 May 2019|archive-date=5 May 2019|archive-url=https://web.archive.org/web/20190505200409/https://www.theverge.com/2019/4/10/18304558/ai-writing-academic-research-book-springer-nature-artificial-intelligence|url-status=live}}</ref> In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19.<ref>{{Cite journal|title=Artificial Intelligence (AI) applications for COVID-19 pandemic|date=1 July 2020|journal=Diabetes & Metabolic Syndrome: Clinical Research & Reviews|volume=14|issue=4|pages=337–339|doi=10.1016/j.dsx.2020.04.012|doi-access=free|last1=Vaishya|first1=Raju|last2=Javaid|first2=Mohd|last3=Khan|first3=Ibrahim Haleem|last4=Haleem|first4=Abid|pmid=32305024|pmc=7195043}}</ref> Machine learning was recently applied to predict the pro-environmental behaviour of travellers.<ref>{{Cite journal|title=Application of machine learning to predict visitors' green behavior in marine protected areas: evidence from Cyprus|first1=Hamed|last1=Rezapouraghdam|first2=Arash|last2=Akhshik|first3=Haywantee|last3=Ramkissoon|date=10 March 2021|journal=Journal of Sustainable Tourism|volume=31 |issue=11 |pages=2479–2505|doi=10.1080/09669582.2021.1887878|doi-access=free|hdl=10037/24073|hdl-access=free}}</ref> Recently, machine learning technology was also applied to optimise smartphone's performance and thermal behaviour based on the user's interaction with the phone.<ref>{{Cite book|last1=Dey|first1=Somdip|last2=Singh|first2=Amit Kumar|last3=Wang|first3=Xiaohang|last4=McDonald-Maier|first4=Klaus|title=2020 Design, Automation & Test in Europe Conference & Exhibition (DATE) |chapter=User Interaction Aware Reinforcement Learning for Power and Thermal Efficiency of CPU-GPU Mobile MPSoCs |date=15 June 2020|chapter-url=https://ieeexplore.ieee.org/document/9116294|pages=1728–1733|doi=10.23919/DATE48585.2020.9116294|isbn=978-3-9819263-4-7|s2cid=219858480|url=http://repository.essex.ac.uk/27546/1/User%20Interaction%20Aware%20Reinforcement%20Learning.pdf |access-date=20 January 2022|archive-date=13 December 2021|archive-url=https://web.archive.org/web/20211213192526/https://ieeexplore.ieee.org/document/9116294/|url-status=live}}</ref><ref>{{Cite news|last=Quested|first=Tony|title=Smartphones get smarter with Essex innovation|work=Business Weekly|url=https://www.businessweekly.co.uk/news/academia-research/smartphones-get-smarter-essex-innovation|access-date=17 June 2021|archive-date=24 June 2021|archive-url=https://web.archive.org/web/20210624200126/https://www.businessweekly.co.uk/news/academia-research/smartphones-get-smarter-essex-innovation|url-status=live}}</ref><ref>{{Cite news|last=Williams|first=Rhiannon|date=21 July 2020|title=Future smartphones 'will prolong their own battery life by monitoring owners' behaviour'|url=https://inews.co.uk/news/technology/future-smartphones-prolong-battery-life-monitoring-behaviour-558689|access-date=17 June 2021|newspaper=[[i (British newspaper)|i]]|language=en|archive-date=24 June 2021|archive-url=https://web.archive.org/web/20210624201153/https://inews.co.uk/news/technology/future-smartphones-prolong-battery-life-monitoring-behaviour-558689|url-status=live}}</ref> When applied correctly, machine learning algorithms (MLAs) can utilise a wide range of company characteristics to predict stock returns without [[overfitting]]. By employing effective feature engineering and combining forecasts, MLAs can generate results that far surpass those obtained from basic linear techniques like [[Ordinary least squares|OLS]].<ref>{{Cite journal |last1=Rasekhschaffe |first1=Keywan Christian |last2=Jones |first2=Robert C. |date=1 July 2019 |title=Machine Learning for Stock Selection |url=https://www.tandfonline.com/doi/full/10.1080/0015198X.2019.1596678 |journal=Financial Analysts Journal |language=en |volume=75 |issue=3 |pages=70–88 |doi=10.1080/0015198X.2019.1596678 |s2cid=108312507 |issn=0015-198X |access-date=26 November 2023 |archive-date=26 November 2023 |archive-url=https://web.archive.org/web/20231126160605/https://www.tandfonline.com/doi/full/10.1080/0015198X.2019.1596678 |url-status=live |url-access=subscription }}</ref>

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 |pages=103140 |doi=10.1016/j.autcon.2020.103140 |hdl=10179/17315 |issn=0926-5805 |access-date=19 May 2024 |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519121548/https://www.sciencedirect.com/science/article/abs/pii/S0926580519313184 |url-status=live |hdl-access=free }}</ref> 

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>