Machine learning for science: state of the art and future prospects.
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.
Mjolsness
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Machine Learning Systems Group, Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA, 91109, USA. mjolsness@jpl.nasa.gov
DeCoste
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D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D055580:Astronomical Phenomena; D001256:Astronomy; D016000:Cluster Analysis; D019295:Computational Biology; D003198:Computer Simulation; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D055585:Physical Phenomena; D010825:Physics; D012371:Robotics