Machine learning: Trends, perspectives, and prospects

@article{Jordan2015MachineLT,
  title={Machine learning: Trends, perspectives, and prospects},
  author={Michael I. Jordan and T. M. Mitchell},
  journal={Science},
  year={2015},
  volume={349},
  pages={255 - 260}
}
  • Michael I. Jordan, T. M. Mitchell
  • Published 2015
  • Computer Science, Medicine
  • Science
    • 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… CONTINUE READING
    View on Science
    ai.arizona.edu
    Top 3 of 1,405 Citations
    Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI
    A. Holzinger, Peter Kieseberg, … A. TjoaCD-MAKE2018
    48
    Machine learning in cardiovascular medicine: are we there yet?
    Khader Shameer, K. Johnson, … P. SenguptaHeart2018
    103
    Machine learning for enterprises: Applications, algorithm selection, and challenges
    I. Lee, Y. J. Shin2020
    9
    1,405 Citations
    Citation Type

    Citation Type

    Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI
    • 48
    • PDF
    Machine learning in cardiovascular medicine: are we there yet?
    • 103
    Machine learning for enterprises: Applications, algorithm selection, and challenges
    • 9
    Interactive machine learning for health informatics: when do we need the human-in-the-loop?
    • 268
    • PDF
    Predictive analytics in health care using machine learning tools and techniques
    • 20
    Machine learning on big data: Opportunities and challenges
    • 219
    • PDF
    Introduction to MAchine Learning & Knowledge Extraction (MAKE)
    • 22
    • PDF
    Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives
    • 10
    • Highly Influenced
    • PDF
    Machine Learning and Financial Planning
    • 5
    How Machine Learning is Changing e-Government
    • 2
    • PDF

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 67 REFERENCES
    Machine learning - a probabilistic perspective
    • 5,423
    • PDF
    Deep learning in neural networks: An overview
    • 7,742
    • PDF
    Reinforcement Learning: An Introduction
    • 25,502
    • PDF
    Foundations and Trends ® in Machine Learning > Vol 7 > Issue 4-5 Ordering Info About Us Alerts Contact Help Log in Adaptation , Learning , and Optimization over Networks
    • 121
    • PDF
    Proceedings of the 22nd international conference on Machine learning
    • 97
    Transfer Learning for Reinforcement Learning Domains: A Survey
    • 1,115
    • PDF
    Computational and statistical tradeoffs via convex relaxation
    • 179
    • PDF
    Using More Data to Speed-up Training Time
    • 29
    • PDF
    A theory of the learnable
    • 3,348
    • PDF
    Randomized Algorithms for Matrices and Data
    • 672
    • PDF