Learning Sets of Rules and Analytical Learning

At the core of Artificial Intelligence, two major pathways of knowledge extraction and representation have been the cornerstone for many decades: Deductive Learning, based on sets of "rules" from Predicate Calculus and Horn clauses that represent the domain experts' knowledge; and Inductive Learning, based on 'generalization by examples' by more or less 'black box' algorithms.
In this third lecture, the Deductive approach is explored via the notion of Explanation-Based Learning (EBL), which is prevalent in Logic Programming and Expert Systems, implemented by languages like Prolog and LISP. Similarly, the Inductive approach is explained via the notion of Analytical Learning, which is prevalent in the last few decades in Pattern Recognition and Machine Learning, more commonly manifested as Neural Networks, Genetic Algorithms, Deep Learning, etc.

Keywords: Machine Learning, Data Analytics, AI, Artificial Intelligence, lecture
Video: https://youtu.be/aNyoGAaa5LQ