Meta-learning, or "learning to learn," is a subfield of machine learning where the goal is to develop models and algorithms that can learn from various tasks and improve their learning process over time. Unlike traditional machine learning methods focusing on learning a specific task, meta-learning aims to leverage experience from previous tasks to enhance future learning. This approach is particularly beneficial in scenarios where the available data for a new task is limited, but there exists abundant data from related tasks. By extracting and utilizing the underlying structure and patterns across these tasks, meta-learning algorithms can achieve faster convergence and better performance with fewer data. The following notes are mainly inspired from \cite{vanschoren2018meta}, \cite{baxter2019learning}, and \cite{maurer2005algorithmic}.

翻译：元学习，或称“学会学习”，是机器学习的一个子领域，其目标是开发能够从多种任务中学习并随时间改进其学习过程的模型与算法。与传统机器学习方法专注于学习特定任务不同，元学习旨在利用先前任务的经验来增强未来的学习能力。这种方法在针对新任务可用数据有限，但存在大量相关任务数据的场景中尤为有益。通过提取并利用这些任务间的底层结构与模式，元学习算法能够以更少的数据实现更快的收敛与更好的性能。以下笔记主要受\cite{vanschoren2018meta}、\cite{baxter2019learning}和\cite{maurer2005algorithmic}的启发。