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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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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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Researcher reasoning meets computational capacity: Machine learning for social science
Social Science Research, 2022 Computational power and big data have created new opportunities to explore and understand the social world. A special synergy is possible when social scientists combine human attention to certain aspects of the problem with the power of algorithms to automate other aspects of the problem. We review selected exemplary applications where machine learning
Ian Lundberg +2 more
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Neuron, 2023 Neuroscience, cognitive science, and computer science are increasingly benefiting through their interactions. This could be accelerated by direct sharing of computational models across disparate modeling software used in each. We describe a Model Description Format designed to meet this challenge.
Gleeson, Padraig +6 more
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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
Amoore, Louise +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.
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Introducing Computer Science Unplugged in Pakistan: A Machine Learning Approach
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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Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning [PDF]
British Journal of Educational Technology, 2020 Abstract Collaborative learning (CL) can be a powerful method for sharing understanding between learners. To this end, strategic regulation of processes, such as cognition and affect (including metacognition, emotion and motivation) is key.
Järvelä, S. (Sanna) +4 more
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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 A. Wallace +2 more
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Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering [PDF]
2019 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS), 2019 Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of data, scientists' expertise, tools, and workflows. If data are not tracked properly during the lifecycle, it becomes unfeasible to recreate a ML model from scratch or to ...
Souza, Renan +12 more
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