  Deep learning has achieved remarkable success in many machine learning tasks
such as image classification, speech recognition, and game playing. However,
these breakthroughs are often difficult to translate into real-world
engineering systems because deep learning models require a massive number of
training samples, which are costly to obtain in practice. To address labeled
data scarcity, few-shot meta-learning optimizes learning algorithms that can
efficiently adapt to new tasks quickly. While meta-learning is gaining
significant interest in the machine learning literature, its working principles
and theoretic fundamentals are not as well understood in the engineering
community.
  This review monograph provides an introduction to meta-learning by covering
principles, algorithms, theory, and engineering applications. After introducing
meta-learning in comparison with conventional and joint learning, we describe
the main meta-learning algorithms, as well as a general bilevel optimization
framework for the definition of meta-learning techniques. Then, we summarize
known results on the generalization capabilities of meta-learning from a
statistical learning viewpoint. Applications to communication systems,
including decoding and power allocation, are discussed next, followed by an
introduction to aspects related to the integration of meta-learning with
emerging computing technologies, namely neuromorphic and quantum computing. The
monograph is concluded with an overview of open research challenges.
