Unit information: Learning from Structured Data in 2009/10

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Unit name Learning from Structured Data
Unit code COMSM0301
Credit points 10
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Peter Flach
Open unit status Not open
Pre-requisites

None
Co-requisites

None
School/department Department of Computer Science
Faculty Faculty of Engineering

Description including Unit Aims

The aim of this unit is to equip you with the knowledge and skills necessary to compare, contrast and adequately apply symbolic machine learning techniques to structured data. This unit extends symbolic machine learning to tasks where the data is highly structured. Inductive logic programming (ILP) is covered in detail. The notion of background knowledge is introduced. Representational issues and trade-offs are discussed, and several representative algorithms are presented. Practical, hands-on experiments are conducted by the students. This unit is aimed at MSc students following the Machine Learning and Data Mining theme.

Aims:

The aim of this unit is to equip students with the knowledge and skills necessary to compare, contrast and adequately apply symbolic machine learning techniques to structured data.

Intended Learning Outcomes

After completing this unit, students will be able to:

1) Use logic and other forms of structured knowledge representation to represent suitable tasks.

2) Apply symbolic machine learning algorithms to structured data.

3) Appreciate the contribution and limitations of the added expressiveness of logical representations.

Teaching Information

Lectures

Assessment Information

20% Coursework 1  test assimilation and understanding of fundamental logical concepts

30% Coursework 2  a more challenging application and exploration of the techniques studied

50% Exam

Reading and References

  • Luc De Raedt. Logical and Relational Learning: from Inductive Logic Programming to Multi-Relational Data Mining. Springer. February 2008. (Essential)
  • S Dzeroski and N Lavrac Relational Data Mining. Springer-Verlag, 2001. (Recommended)
  • N Lavrac and S Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994. (Recommended)

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