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

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

Introduction

24-25 January 2007


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!

24-25 January 2007


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• …

24-25 January 2007


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…

24-25 January 2007


Example 1

24-25 January 2007


Example 2

24-25 January 2007


Example 3

24-25 January 2007


Example 4

24-25 January 2007


Data modelling problems

• Examples 1,2 – regression.

• Example 3 – classification/pattern recognition.

• Example 4 – density estimation.

This course - where do you put the line?

24-25 January 2007


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?

24-25 January 2007


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.

24-25 January 2007


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.

24-25 January 2007


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.

24-25 January 2007


Notation

1{ }Ni iy

1{ } ,Ni ix

{0,1}y classification

regressiony R

nx R

,1 ,2 ,, , ,T

i i i i mx x x x Input variables

Inputs

Outputs

Targets 1

N

i iz

Possible values as per y

24-25 January 2007


Basic problem

( )y f xz y e

Given

Density estimation requires a more complicated notation – given as required.

where e is noise.

Estimate f from 1{ , } .Ni i ix z

24-25 January 2007


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 athttp://www.shef.ac.uk/acse/events/

datamodellingcourse.html

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

Documents

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