Monday 6 March

09:30 - 10:00

Introduction

This introduction will present a brief overview of the main challenges of the Data Analysis in HEP and how Computational Intelligence methods can help in addressing these challenges. It will provide the general background that will allow the audience to put in the context the specific information presented in the lectures of the series.

The introduction together with the whole series of lectures target both particle physicists and computer scientists.  They are meant as an inspiration and encouragement for particle physicists to explore new algorithms and as an invitation for computer scientists to propose other powerful algorithms developed in their field.

Feature Selection and Statistical Learning Basics

Feature Selection and Statistical Learning Basics

Selecting meaningful features is as important for the success of a classification or regression task as the intelligent system used for the actual work. This lecture shows you important aspects in feature selection and introduces the basics of classification with the Bayesian theorem. This is done while walking through the data analysis process, from the initial features to the classification decision.

The lecture targets physicists and computer scientists alike and lays some of the groundwork for the following lectures.

Motivation
- The Data Analysis Process

- Why Attribute Selection and Data Preparation is important

Strategies for Feature Selection

- Reducing the Dimensionality of the feature space with the principal Component Analysis (Karhunen-Loeve Transformation)

- Measuring the Importance of a Feature with Information Gain and Significance Measures1

Statistical Learning Basics

- Optimal classification: Bayes theorem

- Bayes Classifier

- Evaluating the performance of a classifier