<< 2008-9 >>
Department of
Computer Science
 

Learning from Structured Data

Timetable: The course outline is shown below. Lectures are on Mondays 3-5pm in MVB 1.11a. There are no supervised labs (although lab space is available on Wednesdays 11am-1pm in MVB 2.11).

Textbook: The course is largely based on the textbook is Logical and Relational Learning by Luc De Raedt. The chapters corresponsing to each lecture are indicated below.

Week 13 Introduction to learning from structured data Chap 1
Week 14 Foundations of logical learning and inference Chap 2
Week 15 Representations for learning and mining Chap 4 Assignment 1(20%) Deadline: 12th March
Week 16 Propositionalisation and flattening Chap 4  
Week 17 Generalisation and specialisation Chap 5  
Week 18 Inductive logic programming and Progol  
Week 19 Upgrading propositional learners Chap 6 Assignment2 (30%) Deadline: 30 April
Week 20 NO LECTURE (Coursework Preparation)  
Easter break  
Week 21 Naive Bayesian classification of structured data  
Week 22 Kernels and distances for structured data Chap 9  
Week 23 Conclusion
Week 24 NO LECTURE (Exam Revision)

Assessment:50% of the course mark comes from two assignments, which will be made accessible from the table above. The other 50% comes from the exam.

Materials: Printed materials will handed out at the start of each leacture and then posted on the table above. Supplementary materials for lectures not in the textbook will also be made available from the table above

Helpdesk: Helpdesk assistance is available from Tarek Abudawood on Wednesdays 11am-12am in MVB 1.09.

Lecturer: To discuss any other matters, please email Oliver Ray to arrange an appointment.

Feedback: The assignments on this unit are mostly open ended, so they will be marked by hand and there will be an overall marking report and a small amount of individual feedback for each one.

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