iCSC2006 Computational Intelligence for HEP Data Analysis: Status and Recent Advances

Coordinators:
Liliana Teodorescu - Brunel University

Anselm Vossen - Albert-Ludwigs-Universiteit

A few questions

Are there new prospects for Machine Learning Techniques in High Energy Physics?
Have you ever heard of Support Vector Machines or Gene Expression Programming? Are they used in High Energy Physics?
Do you know the current status of the Artificial Neural Networks applications in High Energy Physics?
Are you aware of the state of the art algorithms for classification, regression or optimisation problems?
Do you know how to evaluate the performance of such algorithms?

All the answers in the Computational Intelligence at iCSC

This lecture series will provide an introduction to advanced data analysis techniques developed in computer science and their current or potential applications in High Energy Physics.

The lectures will cover basic topics of Machine Learning Techniques and detailed discussions of how to prepare data, how to extract meaningful attributes, and how to evaluate algorithms performance.

Artificial Neural Networks and their current status in High Energy Physics will be presented as an example of a technique becoming more and more common in this field. Other state of the art algorithms such as Support Vector Machines and new developments in Evolutionary Computation will also be introduced and their prospects in High Energy Physics will be discussed.

Overview

Slot	Lecture	Description	Lecturer
		Monday 6 March
09:30- 10:00		Introduction	Liliana Teodorescu
10:00 - 11:00	Lecture 1	Feature Selection and Statistical Learning Basics	Anselm Vossen
11:30 - 12:25	Lecture 2	Basic Machine Learning Algorithms	Jaroslaw Prybyszewski
12:30 - 14:00		Lunch
14:00 - 14:55	Lecture 3	Neural Networks	Liliana Teodorescu
15:05 - 16:00	Lecture 4	Support Vector Machines	Anselm Vossen
16:30 - 17:25	Lecture 5	Evolutionary Computation	Liliana Teodorescu
17:30		Adjourn