WARNING: This article was written by the author during high-school, in a non-professional capacity. Meta-learning, or learning to learn, is a paradigm of machine learning algorithms that can generalize itself with meta-knowledge of a certain form such that it can apply to various settings. While it is originally a hallmark of human intelligence, numerous meta-learning perspectives and approaches are springing up in recent years. This paper provides an overview of recent meta-learning approaches, especially for Model-Agnostic Meta-Learning (and its derivatives), Meta-Reinforcement Leaning, and Few-shot (or Zero/One-shot), three emerging methods in the past five years.