Machine learning: Trends, perspectives, and prospects

Машинное обучение: тенденции, перспективы и прогнозы
Tom M. Mitchell, Michael I. Jordan
2015-07-16

applications in healthcare, manufacturing, education, finance, policing, marketingdata-intensive methodslearning algorithmsmachine learningstatistical learning theory
Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
1
Data-intensive machine-learning methods are increasingly adopted across domains (healthcare, manufacturing, education, finance, policing, marketing).
2
Machine learning enables computers to improve automatically through experience, addressing core AI and data science problems.
3
Recent progress is driven by new learning algorithms/theory and massive increases in online data and low-cost computation.
4
Wider adoption of machine learning promotes more evidence-based decision-making across science, technology, and commerce.

Machine learning

Trends, perspectives, and prospects of machine learning including algorithmic and theoretical developments, data- and compute-driven growth, and applications across science, technology, and commerce

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Publication Date
2015-07-16
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Authors
Tom M. Mitchell
Michael I. Jordan
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