Machine learning for science: state of the art and future prospects.

@article{Mjolsness2001MachineLF,
  title={Machine learning for science: state of the art and future prospects.},
  author={Ekic Mjolsness and Dennis DeCoste},
  journal={Science},
  year={2001},
  volume={293 5537},
  pages={2051-5}
}
Recent advances in machine learning methods, along with successful applications across a wide variety of fields such as planetary science and bioinformatics, promise powerful new tools for practicing scientists. This viewpoint highlights some useful characteristics of modern machine learning methods and their relevance to scientific applications. We conclude with some speculations on near-term progress and promising directions. 

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