Short answers, pulled from the story.
Computational learning theory emerged as a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms. This field asks fundamental questions about what can be learned and how efficiently through inductive learning processes where systems infer general rules from specific examples.
Publications from 1988 onwards show increasing convergence between pure theory and engineering needs for computational learning theory. Surveys published in 1992 documented these selected bibliographies highlighting key contributions to the transition from abstract models to working software.
Dana Angluin proposed exact learning as a method requiring perfect accuracy from limited data samples within computational learning theory. Leslie Valiant introduced probably approximately correct learning which allows for small error margins with high probability while Vladimir Vapnik and Alexey Chervonenkis developed VC theory focusing on function class capacity.
In this field, a computation is considered feasible only if it can be executed in polynomial time according to computational learning theory standards. Researchers categorize findings into positive results demonstrating learnable functions and negative results proving other classes cannot be learned efficiently under similar conditions.
Several different approaches exist based on varying assumptions about inference principles including online machine learning brought forward by Nick Littlestone. Each approach defines probability differently using either frequency or Bayesian frameworks and makes distinct assumptions regarding how samples are generated during the learning process.