Monday 6 March

This lecture will present the fundamentals of the Artificial Neural Networks and examples of their applications in HEP data analysis. The examples will show the development cycle of these algorithms in HEP: a slow and late start followed by  a gradually increasing presence and acceptance in the physicists’ community.

The lecture targets both physicists and computer scientists interested in algorithms for data analysis.

A minimal general background in particle physics data analysis techniques is sufficient for understanding the topic. No a priory knowledge on Artificial Neural Networks is required.

Introduction 

- Biological Neural Networks

- Artificial Neural Networks (NN)

Basics of NN

- Artificial neuron

- Percepton

- Classification of NN

Operation of NN

- Learning types and rules

- Learning and testing

Examples of NN

- Feed-forward NN

- Recurrent NN

- Functional NN

Performance Issues

- Performance factors and measures

- Analysis of performance

Examples of NN in HEP

- NN triggers

- NN  for offline data analysis applications

Pro's and Con's  NN in HEP

Post-reg.

Computational Intelligence
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Parallel Application

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SW Testing
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