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
收藏
页码：35 / 39
页数：5
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