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* [[Precision agriculture|Agriculture]]

* [[Precision agriculture |Agriculture]] 精准农业

* [[Computational anatomy|Anatomy]]

* [[Computational anatomy |Anatomy]] 计算解剖学

* [[Adaptive website]]s

* [[Adaptive website ]]s 自适应站点

* [[Affective computing]]

* [[Affective computing]] 情感计算

* [[Banking]]

* [[Banking]] 银行

* [[Bioinformatics]]

* [[Bioinformatics]] 生物信息学

* [[Brain–machine interface]]s

* [[Brain–machine interface]]s 脑-机接口

* [[Cheminformatics]]

* [[Cheminformatics]] 化学信息学

* [[Citizen science]]

* [[Citizen science]] 公民科学

* [[Network simulation|Computer networks]]

* [[Network simulation|Computer networks]] 网络仿真

* [[Computer vision]]

* [[Computer vision]] 计算机视觉

* [[Credit-card fraud]] detection

* [[Credit-card fraud]] detection 信用卡欺诈检测

* [[Data quality]]

* [[Data quality]] 数据质量

* [[DNA sequence]] classification

* [[DNA sequence]] classification DNA序列分类

* [[Computational economics|Economics]]

* [[Computational economics|Economics]] 计算经济学

* [[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"].</ref>

* [[Financial market 金融市场]] 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"].</ref>

* [[General game playing]]

* [[General game playing]] 一般游戏

* [[Handwriting recognition]]

* [[Handwriting recognition]] 手写体识别

* [[Information retrieval]]

* [[Information retrieval]] 情报检索

* [[Insurance]]

* [[Insurance]] 保险

* [[Internet fraud]] detection

* [[Internet fraud]] detection 网络欺诈检测

* [[Computational linguistics|Linguistics]]

* [[Computational linguistics|Linguistics]] 计算语言学

* [[Machine learning control]]

* [[Machine learning control]] 机器学习控制

* [[Machine perception]]

* [[Machine perception]] 机器感知

* [[Machine translation]]

* [[Machine translation]] 机器翻译

* [[Marketing]]

* [[Marketing]] 营销

* [[Automated medical diagnosis|Medical diagnosis]]

* [[Automated medical diagnosis|Medical diagnosis]] 自动医疗诊断

* [[Natural language processing]]

* [[Natural language processing]] 自然语言处理

* [[Natural language understanding]]

* [[Natural language understanding]] 自然语言理解

* [[Online advertising]]

* [[Online advertising]] 在线广告

* [[Mathematical optimization|Optimization]]

* [[Mathematical optimization|Optimization]] 数学优化

* [[Recommender system]]s

* [[Recommender system]]s 推荐系统

* [[Robot locomotion]]

* [[Robot locomotion]] 机器人运动

* [[Search engines]]

* [[Search engines]] 搜索引擎

* [[Sentiment analysis]]

* [[Sentiment analysis]] 情绪分析

* [[Sequence mining]]

* [[Sequence mining]] 序列挖掘

* [[Software engineering]]

* [[Software engineering]] 软件工程

* [[Speech recognition]]

* [[Speech recognition]] 语音识别

* [[Structural health monitoring]]

* [[Structural health monitoring]] 结构健康监测

* [[Syntactic pattern recognition]]

* [[Syntactic pattern recognition]] 句法模式识别

* [[Telecommunication]]

* [[Telecommunication]] 电信

* [[Automated theorem proving|Theorem proving]]

* [[Automated theorem proving|Theorem proving]] 自动定理证明

* [[Time series|Time series forecasting]]

* [[Time series|Time series forecasting]] 时间序列预测

* [[User behavior analytics]]

* [[User behavior analytics]] 用户行为分析

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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 on 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 model to win the Grand Prize in 2009 for $1&nbsp;million. Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis. 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. 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 unrecognized influences among artists. In 2019 Springer Nature published the first research book created using machine learning.

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 on 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 model to win the Grand Prize in 2009 for $1&nbsp;million. Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis. 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. 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 unrecognized influences among artists. In 2019 Springer Nature published the first research book created using machine learning.

2006年，媒体服务提供商 Netflix 举办了首届“ Netflix 大奖”竞赛，目的是找到一个能更好地预测用户偏好的程序，并将其现有的 Cinematch 电影推荐算法的准确性提高至少10% 。由 at & t 实验室的研究人员组成的联合团队与 Big Chaos 和 Pragmatic Theory 团队合作建立了一个集成模型，赢得了2009年的一百万美元大奖。在该奖项颁发后不久，Netflix 意识到观众的收视率并不是他们观看模式的最佳指标(“一切都是推荐”) ，于是他们相应地改变了自己的推荐引擎。2010年，《华尔街日报》报道了 Rebellion Research 公司及其利用机器学习预测金融危机的情况。2012年，昇阳电脑的联合创始人 Vinod Khosla 预测，在未来的20年里，80% 的医生的工作将会因为自动化的机器学习医疗诊断软件而流失。2014年，据报道，一种机器学习算法已应用于艺术史领域，用于研究美术绘画，它可能揭示了艺术家之间以前未被认识到的影响。2019年，施普林格 · 自然出版了第一本利用机器学习进行研究的书。

2006年，媒体服务提供商 Netflix 举办了首届“ Netflix 大奖”竞赛，目的是找到一个能更好地预测用户偏好的程序，并将其现有的 Cinematch 电影推荐算法的准确性提高至少10% 。由 AT&Tt 实验室的研究人员组成的联合团队与 Big Chaos 和 Pragmatic Theory 的团队合作建立了一个集成模型，赢得了2009年的一百万美元大奖。在该奖项颁发后不久，Netflix 意识到观众的收视率并不是他们观看模式的最佳指标（“一切都是推荐”），于是他们相应地改变了自己的推荐引擎。2010年，《华尔街日报》报道了 Rebellion Research 公司及其利用机器学习预测金融危机的情况。2012年，昇阳电脑的联合创始人 Vinod Khosla 预测，在未来的20年里，80%的医生的工作将会因为自动化的机器学习医疗诊断软件而流失。2014年，据报道，一种机器学习算法已应用于艺术史领域，用于研究美术绘画，它可能揭示了艺术家们前所未有的影响。2019年，Springer Nature出版了第一本利用机器学习进行研究的书。

 

 

== 局限性Limitations ==

== 局限性Limitations ==