an overview of state-of-the-art data modelling
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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?. - PowerPoint PPT PresentationTRANSCRIPT
An Overview of State-of-the-Art Data Modelling
Introduction
24-25 January 2007
An 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!
24-25 January 2007
An 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• …
24-25 January 2007
An 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…
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 1
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 1
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 1
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 1
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 1
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 1
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 2
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 2
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 2
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 3
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 3
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 4
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
Example 4
24-25 January 2007
An 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?
24-25 January 2007
An 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?
24-25 January 2007
An 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.
24-25 January 2007
An 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.
24-25 January 2007
An 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.
24-25 January 2007
An Overview of State-of-the-Art Data Modelling
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
An Overview of State-of-the-Art Data Modelling
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
An 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 athttp://www.shef.ac.uk/acse/events/
datamodellingcourse.html