Machine Learning and Case-Based Reasoning

The course gives an introduction to the principles and methods for automatic learning in computer systems. Classical syntax-based learning methods as well as more knowledge-intensive methods are described. Main empahsis is on symbolic methods, where explicit concepts and relationships are learned. Statistical methods and reinforcement learning is also included. The strengths and weaknesses of various methods are compared.

Learning methods in case-based reasoning and the integration of learning and problem solving is given particular treatment. Numerical and cognitive models for similarity asessment will be discussed, together with different learning system architectures. Methods that combine case-based and generalisation-based inferences will be discussed as well.

The aim of the course is to introduce principles of machine learning methods in general and case-based methods in particular, to students with a basic knowledge of AI methods.

Lectures, colloquia, self study, exercises.