Authorised by Academic Registrar, April 1996
Objectives On completion of the subject students will appreciate the role of machine learning in AI theory and applications; understand the main current techniques used to implement machine learning; be able to design and implement a machine learning program.
Synopsis This subject examines the main contending approaches taken to the computational modelling of learning. Topics include production systems as cognitive models and their evolution into expert systems; version space approaches to concept formation; explanation-based learning; `scientific discovery' (data-driven) methods of learning empirical laws. The subject takes up various quantitative methods of learning, especially information-theoretic methods of classification and Bayesian learning techniques. Also covered are evolutionary programming (genetic algorithms), connectionism (neural networks), and the alleged incompatibilities between these different models.
Assessment Examination (2 hours): 50% + Programming project: 50%