CyberTraining: Pilot: Machine Learning Foundations and Applications in the Earth Systems Sciences

在地球系统科学（ESS）中越来越多地使用机器学习技术对科学和工程研究产生了积极的影响，但易用性加上许多常用工具的不透明性可能会导致信任，而不会对工具的输出进行严格评估。围绕机器学习工具的理论基础的教育需要额外的，通常是实质性的高级数学和编程课程。无法访问此背景信息的网络基础设施用户可能很难对机器学习技术的实用性和适当性建立欣赏和理解，特别是当他们参加资源不足的机构时，无法自己创建相关的教育材料。该项目旨在帮助大学水平的学习者建立必要的网络基础设施素养和技能，使他们能够适当地将机器学习技术应用于ESS研究，而不需要禁止额外的课程。这些未来的网络基础设施用户受益于机器学习技术的适当和道德使用，即使使用的工具是由其他人开发的。该项目的目标是阐明机器学习模型背后的概念机制，为大学水平的ESS学生和早期职业生涯的专业人士，并弥合机器学习概念和低代码之间的差距差距，在地球系统科学的现实世界中的应用。它将通过提供一系列三个学习模块来实现这一目标：（1）自定进度的概念介绍，使用系统思维方法来理解机器学习如何在ESS中工作，（2）自定进度的低代码模块，使学习者能够将概念框架应用于具有相关ESS数据的真实的世界场景，以及（3）基于实验室的活动，促进小组讨论，决策的合理性，以及对机器学习技术和输出的批判性分析。这种设计非常适合与现有课程相结合的翻转课堂设置，允许学习者练习这些技能，而无需承担额外的课程。此外，该计划还培养了ESS网络基础设施用户的关键判断技能，进一步促进了广泛的，战略的，道德的，以及网络基础设施和机器学习的适当使用。高级网络基础设施办公室的这一奖项由地球科学理事会共同支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

The increasing use of machine learning techniques in Earth Systems Science (ESS) has positive impacts on science and engineering research, but ease of use coupled with the opaque nature of many commonly available tools can lead to trust without critical assessment of the tools' outputs. Education surrounding the theoretical underpinnings of machine learning tools requires additional, and often substantive, coursework in advanced math and programming. Cyberinfrastructure users without access to this background information may have difficulty building an appreciation and understanding of the utility and appropriateness of machine learning technology for their research and career goals, especially when they attend under-resourced institutions that cannot create relevant educational materials on their own. This project intends to help university-level learners build necessary cyberinfrastructure literacy and skills that will allow them to appropriately apply machine learning techniques to their ESS research without requiring prohibitive additional coursework. These future cyberinfrastructure users benefit from practicing appropriate and ethical usage of machine learning techniques even when the tools in use were developed by others. The goals of this project are to elucidate the conceptual mechanisms behind machine learning models for university-level ESS students and early-career professionals, and to bridge the gap between machine learning concepts and low-code, real-world applications in the Earth Systems Sciences. It will accomplish this by providing a series of three learning modules: (1) a self-paced conceptual introduction that uses a systems-thinking approach to understanding how machine learning works in ESS, (2) a self-paced, low-code module that enables learners to apply the conceptual frameworks to real world scenarios with relevant ESS data, and (3) a lab-based activity that promotes group discussion, justification of decision making, and critical analysis of machine learning techniques and outputs. This design lends itself well to a flipped classroom setting integrated with existing curricula, allowing learners to practice these skills without the need to take on additional coursework. Additionally, the program fosters critical judgment skills in ESS cyberinfrastructure users, furthering the broad, strategic, ethical, and appropriate usage of cyberinfrastructure and machine learning.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Directorate for Geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.