Results 11 to 20 of about 1,130,914 (300)
Introducing Computer Science Unplugged in Pakistan: A Machine Learning Approach [PDF]
Education Sciences, 2023 Introducing computational thinking at elementary school can develop students’ capabilities and interest in Computing skills. In this study, we introduced the Computer Science unplugged (CS-unplugged) technique in Pakistan. We use paper-based activities to equip students with basic Computer Science skills without introducing any programming language ...
Seema Jehan, Pakeeza Akram
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Machine learning, meaning making: On reading computer science texts
Big Data & Society, 2023 Computer science tends to foreclose the reading of its texts by social science and humanities scholars – via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not
Louise Amoore +3 more
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Computer science: The learning machines [PDF]
Nature, 2014 Using massive amounts of data to recognize photos and speech, deep-learning computers are taking a big step towards true artificial intelligence.
N.B. Jones
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ML ALGORITHMS CATEGORIZATION AND INTERSECTIO N OF STATISTICS AND COMPUTER SCIENCE IN MACHINE LEARNING [PDF]
JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, 2020 V. Pranathi
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Integrating games and machine learning in the undergraduate computer science classroom [PDF]
Proceedings of the 3rd international conference on Game development in computer science education, 2008 A student will be more likely motivated to pursue a field of study if they encounter relevant and interesting challenges early in their studies. The authors are PIs on two NSF funded course curriculum development projects (CCLI). Each project seeks to provide compelling curricular modules for use in the Computer Science classroom starting as soon as CS
Scott Wallace +2 more
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PLOS ONE, 2021 Searching for new high temperature superconductors has long been a key research issue. Fe-based superconductors attract researchers’ attention due to their high transition temperature, strong irreversibility field, and excellent crystallographic symmetry.
Zhiyuan Hu
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Perspective on integrating machine learning into computational chemistry and materials science [PDF]
The Journal of Chemical Physics, 2021 Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and ...
Julia Westermayr +3 more
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REFORMS: Reporting Standards for Machine Learning Based Science [PDF]
arXiv.org, 2023 Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability.
Sayash Kapoor +18 more
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Machine-Learning Methods for Computational Science and Engineering [PDF]
Computation, 2020 The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering.
Michael Frank +2 more
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Data-driven science and machine learning methods in laser–plasma physics [PDF]
High Power Laser Science and Engineering, 2022 Laser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available. Early experimental and numerical research in this field was dominated by single-shot experiments with limited ...
A. Döpp +5 more
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