天文学家机器学习指南
An Astronomers Guide to Machine Learning
AI总结：
本文为天文学家提供机器学习入门指南，概述学习算法类型，并结合DWF计划的光变曲线与光学成像数据案例，提供在线笔记本演示，帮助规避常见陷阱。
摘要：
随着天文数据的数量和可用性迅速增长，天文学家很快将在日常工作中依赖机器学习算法的使用。本会议录旨在概述什么是机器学习，深入探讨多种不同类型的学习算法，并考察两个天文应用案例。机器学习为我们这些处理大量数据的天文学家打开了充满可能性的世界，然而，如果不小心，用户可能会陷入常见的陷阱。在这里，我们将重点解决与时间序列光变曲线数据和光学成像数据相关的问题，这些数据主要来自“更深、更宽、更快”计划（DWF）。除书面示例外，还将提供在线笔记本以演示这些不同的技术。本指南旨在帮助您构建一个小型知识和工具包，供您带回并在未来的机器学习项目中使用。
英文摘要：
With the volume and availability of astronomical data growing rapidly, astronomers will soon rely on the use of machine learning algorithms in their daily work. This proceeding aims to give an overview of what machine learning is and delve into the many different types of learning algorithms and examine two astronomical use cases. Machine learning has opened a world of possibilities for us astronomers working with large amounts of data, however if not careful, users can trip into common pitfalls. Here we'll focus on solving problems related to time-series light curve data and optical imaging data mainly from the Deeper, Wider, Faster Program (DWF). Alongside the written examples, online notebooks will be provided to demonstrate these different techniques. This guide aims to help you build a small toolkit of knowledge and tools to take back with you for use on your own future machine learning projects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
