COURSE
Advanced Topics for Materials Informatics
Function Hub / Function Hub Core · 3 Credits
Materials informatics integrates materials science and engineering with artificial intelligence (AI), machine learning (ML), and database to accelerate the innovation in the whole materials development continuum and to speed up the process from data to material knowledge. This is a continued course based on FUNH 5100 Materials Informatics but with separate topics, where we will introduce advanced AI and ML algorithms with more focus on deep learning, through vivid and practical examples from materials science and engineering. Notably, this course is designed for students whose majors are not in mathematics or computer science but other broader fields, such as materials science and engineering, mechanics science and engineering, physics, and chemistry. This course will empower those with limited knowledge of deep learning to learn Materials Informatics without difficulty.
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仅展示 2026-27 Fall；历史教学班可通过学期筛选查看。
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3 Credits
官方数据更新时间: 2026-08-28 05:35:00
背景: Students who have taken FUNH 5100 are encouraged to take this course for continued advanced topics related to deep learning. However, any students with background/interest in computation/machine learning are welcome to take this course without having taken FUNH 5100. FUNH5100
3 Credits
官方数据更新时间: 2026-08-06 14:55:26
背景: Students who have taken FUNH 5100 are encouraged to take this course for continued advanced topics related to deep learning. However, any students with background/interest in computation/machine learning are welcome to take this course without having taken FUNH 5100. FUNH5100
3 Credits
官方数据更新时间: 2026-08-06 14:55:26
背景: Students who have taken FUNH 5100 are encouraged to take this course for continued advanced topics related to deep learning. However, any students with background/interest in computation/machine learning are welcome to take this course without having taken FUNH 5100. FUNH5100
3 Credits
官方数据更新时间: 2026-08-06 14:55:26
背景: Students who have taken FUNH 5100 are encouraged to take this course for continued advanced topics related to deep learning. However, any students with background/interest in computation/machine learning are welcome to take this course without having taken FUNH 5100. FUNH5100
3 Credits
官方数据更新时间: 2026-08-06 14:55:26
背景: Students who have taken FUNH 5100 are encouraged to take this course for continued advanced topics related to deep learning. However, any students with background/interest in computation/machine learning are welcome to take this course without having taken FUNH 5100. FUNH5100
3 Credits
官方数据更新时间: 2026-08-06 14:55:26
背景: Students who have taken FUNH 5100 are encouraged to take this course for continued advanced topics related to deep learning. However, any students with background/interest in computation/machine learning are welcome to take this course without having taken FUNH 5100. FUNH5100
3 Credits
官方数据更新时间: 2026-08-06 14:55:26
背景: Students who have taken FUNH 5100 are encouraged to take this course for continued advanced topics related to deep learning. However, any students with background/interest in computation/machine learning are welcome to take this course without having taken FUNH 5100. FUNH5100
3 Credits
官方数据更新时间: 2026-08-06 14:55:26
背景: Students who have taken FUNH 5100 are encouraged to take this course for continued advanced topics related to deep learning. However, any students with background/interest in computation/machine learning are welcome to take this course without having taken FUNH 5100. FUNH5100