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An Overview of State-of-the-Art Data Modelling

An Overview of State-of-the-Art Data Modelling. Introduction. 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!. Why data modelling?.

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An Overview of State-of-the-Art Data Modelling

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

  2. 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! An Overview of State-of-the-Art Data Modelling

  3. 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 • … An Overview of State-of-the-Art Data Modelling

  4. 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… An Overview of State-of-the-Art Data Modelling

  5. Example 1 An Overview of State-of-the-Art Data Modelling

  6. Example 1 An Overview of State-of-the-Art Data Modelling

  7. Example 1 An Overview of State-of-the-Art Data Modelling

  8. Example 1 An Overview of State-of-the-Art Data Modelling

  9. Example 1 An Overview of State-of-the-Art Data Modelling

  10. Example 1 An Overview of State-of-the-Art Data Modelling

  11. Example 2 An Overview of State-of-the-Art Data Modelling

  12. Example 2 An Overview of State-of-the-Art Data Modelling

  13. Example 2 An Overview of State-of-the-Art Data Modelling

  14. Example 3 An Overview of State-of-the-Art Data Modelling

  15. Example 3 An Overview of State-of-the-Art Data Modelling

  16. Example 4 An Overview of State-of-the-Art Data Modelling

  17. Example 4 An Overview of State-of-the-Art Data Modelling

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

  19. 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? Different types of learning An Overview of State-of-the-Art Data Modelling

  20. 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. An Overview of State-of-the-Art Data Modelling

  21. 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. An Overview of State-of-the-Art Data Modelling

  22. 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. An Overview of State-of-the-Art Data Modelling

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

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

  25. 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 An Overview of State-of-the-Art Data Modelling

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