1. An Overview of State-of- the-Art Data Modelling Introduction

2. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Aim To provide researchers and practitioners with an overview of state-of-the-art techniques in data modelling. But… We will also show you how to use traditional techniques well!

3. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Why data modelling? Increasingly important to success of many practical applications: Engineering Ecology Chemistry/chemical engineering Financial services Crime prevention Internet search Systems biology Medical diagnosis …

4. 24-25 January 2007An Overview of State-of-the-Art Data Modelling So what is data modelling? Different things to different people. Structuring and organising data. Physical models of data. Models to predict unseen data. For this course consider some examples…

5. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 1

6. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 1

7. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 1

8. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 1

9. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 1

10. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 1

11. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 2

12. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 2

13. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 2

14. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 3

15. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 3

16. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 4

17. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Example 4

18. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Data modelling problems Examples 1,2 – regression. Example 3 – classification/pattern recognition. Example 4 – density estimation. This course - where do you put the line?

19. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Different types of learning Supervised vs unsupervised Do you have target data? Learning with/without a teacher Batch, incremental, sequential, online… Are all the data available initially? Are the data processed one at a time?

20. 24-25 January 2007An Overview of State-of-the-Art Data Modelling The course Focus on supervised learning for regression and classification. Cover density estimation implicitly. Emphasis is on the concepts, ideas and tools… …not, the detailed mathematics.

21. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Day 1 8.30-9.00: Arrival and coffee. 9.00-10.00: Introduction to data modelling. Curve fitting. Regression. Classification. Supervised and unsupervised learning. (Tony Dodd, Department of Automatic Control & Systems Engineering) 10.00-11.00: Linear models. Polynomials. Radial basis functions. (Tony Dodd) 11.00-11.30: Coffee and discussion. 11.30-13.00: Issues in data modelling. Overfitting. Generalisation. Regularisation. Validation. Input selection. Data pre-processing. (Rob Harrison, Department of Automatic Control & Systems Engineering) 13.00-14.00: Lunch. 14.00-15.30: Multi-layer perceptron. (Rob Harrison) 15.30-16.30: Coffee and discussion.

22. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Day 2 8.30-9.00: Coffee. 9.00-10.30: Bayesian methods. Priors. Gaussian processes. (John Paul Gosling, Department of Probability and Statistics) 10.30-11.00: Coffee and discussion. 11.00-12.30: MCMC methods for data modelling. (Kenneth Scerri, Department of Automatic Control & Systems Engineering) 12.30-13.30: Lunch. 13.30-15.00: Kernel methods. Maximum-margin classification. Support vector machines. Sparse data modelling. (Tony Dodd) 15.00-15.30: Coffee and discussion. 15.30-16.30: Algorithms for sequential problems. (Mahesran Niranjan, Department of Computer Science) 16.30-17.00: Discussion and round-up.

23. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Notation classification regression Input variables Inputs Outputs Targets Possible values as per y

24. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Basic problem Given Density estimation requires a more complicated notation – given as required. where e is noise. Estimate f from

25. 24-25 January 2007An Overview of State-of-the-Art Data Modelling Finally… Ask questions. The course is for you. Use the breaks to network and discuss your work. Administrative matters. Useful links http://www.shef.ac.uk/acse/research/cdmg/links/ Notes will be available at http://www.shef.ac.uk/acse/events/datamodellingcourse.html