A high-bias, low-variance introduction to Machine Learning for physicists

  title={A high-bias, low-variance introduction to Machine Learning for physicists},
  author={P. Mehta and M. Bukov and Ching-Hao Wang and A. G. R. Day and C. Richardson and C. Fisher and D. Schwab},
  journal={Physics reports},
  • P. Mehta, M. Bukov, +4 authors D. Schwab
  • Published 2019
  • Physics, Medicine, Computer Science, Mathematics
  • Physics reports
  • Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in… CONTINUE READING
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    Publications referenced by this paper.
    Adam: A Method for Stochastic Optimization
    • 49,762
    • PDF
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    • 50,397
    • PDF
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    • 47,166
    • PDF
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    • 10,958
    • PDF
    Dropout: a simple way to prevent neural networks from overfitting
    • 19,070
    • PDF
    Scikit-learn: Machine Learning in Python
    • 25,518
    • PDF
    Machine learning - a probabilistic perspective
    • 5,426
    • PDF