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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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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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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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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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CancersArtificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited ...
Michele Avanzo +4 more
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, 2019 In the ML fairness literature, there have been few investigations through the viewpoint of philosophy, a lens that encourages the critical evaluation of basic assumptions. The purpose of this paper is to use three ideas from the philosophy of science and computer science to tease out blind spots in the assumptions that underlie ML fairness: abstraction,
Samuel Deng, Achille C. Varzi
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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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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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Categorisation of Computer Science Research Papers using Supervised Machine Learning Techniques [PDF]
International Journal of Computing and Digital Systems, 2020 Hemrajsingh Gheeseewan +1 more
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