  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}.
