Personal notes for CSE20 - Discrete Mathematics for Computer Science. Taken Fall 2020, with professor Shachar Lovett. See Course Homepage Sets Recursive definition of sets Basis step: Specify finitely many elements of \(S\) Recursive step: Give a rule for creating a new element of \(S\) from known values existing in \(S\), and potentially other values. String The set \(\Sigma^*\)of strings over the alphabet \(\Sigma\) is defined recursively by Basis step: \(\lambda \in \Sigma^*\) (where \(\lambda\) is the empty string containing no symbols)

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.