Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges

被引：0

作者：

Acquaviva, Viviana ^{[1
,2
]}

机构：

[1] NYC Coll Technol, Dept Phys, 300 Jay St, Brooklyn, NY 11201 USA

[2] Flatiron Inst, Ctr Computat Astrophys, New York, NY 10010 USA

来源：

SECOND TEACHING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE WORKSHOP, VOL 170 | 2021年 / 170卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This 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.

引用

页码：35 / 39

页数：5

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