Summer school on Machine Learning in High Energy Physics
MLHEP summer school is intended to cover the relatively young area of data analysis and computational research that has started to emerge in High Energy Physics (HEP). It is known by several names including “Multivariate Analysis”, “Neural Networks”, “Classification/Clusterization techniques”. In more generic terms, these techniques belong to the field of “Machine Learning”, which is an area that is based on research performed in Statistics and has received a lot of attention from the Data Science community.
There are plenty of essential problems in High energy Physics that can be solved using Machine Learning methods. These vary from online data filtering and reconstruction to offline data analysis.
Students of the school will receive a theoretical and practical introduction to this new field and will be able to apply their acquired knowledge to solve their own problems.
Expected number of students for the school is 40-50 people (separated equally between Introductory and Advanced tracks).
Pre-requisites for participation:
- Python, C++ programming experience
- interest and background in HEP