Add to ORKGThis paper summarizes some challenges encountered and best practices established in several years of teaching Machine Learning for the Physical Sciences at the undergraduate and graduate level. I discuss motivations for teaching ML to physicists, desirable properties of pedagogical materials, such as accessibility, relevance, and likeness to real-world research problems, and give examples of components of teaching units
Machine learning (ML) provides a powerful framework for the analysis of high-dimensional datasets by...
University of Minnesota Ph.D. dissertation.July 2020. Major: Computer Science. Advisor: Vipin Kumar...
Data is the primary source to scaffold physics teaching and learning for teachers and students, main...
This paper summarizes some challenges encountered and best practices established in several years of...
International audienceMachine learning (ML) encompasses a broad range of algorithms and modeling too...
In most fields of physics, machine learning (ML) is all the rage. Physicists use ML algorithms to an...
The use of computational algorithms, implemented on a computer, to extract information from data has...
Machine learning, and most notably deep neural networks, have seen unprecedented success in recent y...
Machine learning (ML) has found immense success in commercial applications such as computer vision a...
Abstract: Machine learning, which builds on ideas in computer science, statistics, and optimization...
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also pe...
Retention of Science, Technology, Engineering, and Mathematics (STEM) students is a serious problem ...
Can a machine learn Machine Learning? This work trains a machine learning model to solve machine lea...
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also pe...
Machine learning (ML) has become highly relevant in applications across all industries, and speciali...
Machine learning (ML) provides a powerful framework for the analysis of high-dimensional datasets by...
University of Minnesota Ph.D. dissertation.July 2020. Major: Computer Science. Advisor: Vipin Kumar...
Data is the primary source to scaffold physics teaching and learning for teachers and students, main...
This paper summarizes some challenges encountered and best practices established in several years of...
International audienceMachine learning (ML) encompasses a broad range of algorithms and modeling too...
In most fields of physics, machine learning (ML) is all the rage. Physicists use ML algorithms to an...
The use of computational algorithms, implemented on a computer, to extract information from data has...
Machine learning, and most notably deep neural networks, have seen unprecedented success in recent y...
Machine learning (ML) has found immense success in commercial applications such as computer vision a...
Abstract: Machine learning, which builds on ideas in computer science, statistics, and optimization...
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also pe...
Retention of Science, Technology, Engineering, and Mathematics (STEM) students is a serious problem ...
Can a machine learn Machine Learning? This work trains a machine learning model to solve machine lea...
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also pe...
Machine learning (ML) has become highly relevant in applications across all industries, and speciali...
Machine learning (ML) provides a powerful framework for the analysis of high-dimensional datasets by...
University of Minnesota Ph.D. dissertation.July 2020. Major: Computer Science. Advisor: Vipin Kumar...
Data is the primary source to scaffold physics teaching and learning for teachers and students, main...