Course content

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 generalizations, time series methods, ensemble methods, and deep learning are also included. The strengths and weaknesses of various methods are discussed. Learning methods in case-based reasoning is integrated with problem solving within the CBR cycle. 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.

Learning outcome

The aim of the course is to introduce principles of machine learning methods in general, to give an understanding of basic mechanisms underlying various specific methods. In case-based reasoning the integration of learning and problem solving is focused.

Learning methods and activities

Lectures, group work, colloquia, self study, exercises.

Recommended previous knowledge

TDT4136 Introduction to Artificial Intelligence, TDT4171 Artificial Intelligence Methods or similar.

Course materials

Text book:

• Tom Mitchell: Machine learning, McGraw Hill, 1997.
• Michael M. Richer and Rosina Weber: Case-Based Reasoning, Springer, 2013.

Selected papers.

Credit reductions