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...

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Bibliographic Details
Published in:Science (American Association for the Advancement of Science) Vol. 293; no. 5537; pp. 2051 - 2055
Main Authors: Mjolsness, Eric, DeCoste, Dennis
Format: Journal Article
Language:English
Published: United States American Society for the Advancement of Science 14.09.2001
American Association for the Advancement of Science
The American Association for the Advancement of Science
Subjects:
Active learning
Aerospace Education
Algorithm
Algorithms
Analysis
Animals
Artificial Intelligence
Astronomical Phenomena
Astronomy
Automation
Bioinformatics
Biotechnology
Cluster Analysis
Computational Biology
Computer pattern recognition
Computer Simulation
Computers
Cytology
Evaluation
Gene Expression Profiling
Gene Expression Regulation
Genes
Genomes
Genomics
Geology
Hypotheses (Scientific method)
Hypothesis
Image Processing, Computer-Assisted
Inference
Inferences
Learning
Machine learning
Methods
Modeling
Multidimensional Scaling
Neural Networks (Computer)
Observation
Pattern Recognition
Physical Phenomena
Physics
Principals
Robotics
Science
Scientific method
Scientific Methodology
Sequencing
Space research
Statistical Inference
Statistics
Statistics (Data)
Viewpoints
Visualization
United States
ISSN:0036-8075, 1095-9203
Online Access:Get full text
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Description
Summary: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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ISSN:0036-8075
1095-9203
DOI:10.1126/science.293.5537.2051