Eighth Machine Learning in High Energy Physics Summer School 2023



Erice, Italy
Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))

The Eighth Machine Learning summer school organised by INFN to be held on 11th - 18th of April 2023 at Erice, Italy.

The school covers 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 acquired knowledge to solve their own problems. Topics ranging from decision trees to deep learning and hyperparameter optimisation will be covered with concrete examples and hands-on tutorials. A special data-science competition will be organised within the school to allow participants to get better feeling of real-life ML applications scenarios.

The school is aimed at PhD students and postdoctoral researchers but also is open to undergraduate students. Please, make sure to read school participation prerequisites.


Upon completion of the school participants would be able to

  • formulate a HEP-related problem in ML-friendly terms;
  • select quality criteria for a given problem;
  • understand and apply principles of widely-used classification models (e.g. boosting, bagging, BDT, neural networks, etc) to practical cases;
  • optimise features and parameters of a given model in efficient way under given restrictions;
  • select the best classifier implementation amongst a variety of ML libraries (scikit-learn, xgboost, deep learning libraries, etc);
  • understand and apply principles of generative model design;
  • define & conduct reproducible data-driven experiments.






Thanks to our partners, we can provide a bit of subsidy for students who are not able to afford the full registration fee out of their own funds. Make sure you apply before the early registration deadline. See registration fee page for details.