This is the first announcement of the Spring School on Machine Learning for High Energy Physics 2023. This year
it will be held in Erice, Italy, on April 11-18, 2023. It targets PhD students and early-stage career postdocs
primarily. Advanced Master's students are also encouraged to apply. The school is organised by INFN and
Practicum. 

The primary goal of the MLHEP school is a focused introduction to applied modern machine learning techniques
that could improve physics performance for various HEP-related problems. The school pays attention to the
student experience, so along with "hands-on" seminars, a dedicated data science competition will be
organised. 

Additionally, the school will include a series of talks that show real examples of improvements for particular
physics cases due to machine learning techniques. It is ideally suited for advanced graduate students and
young postdocs willing to learn how to:

        • formulate HEP-related problems in machine learning-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 the given model in an efficient way under given restrictions;
        • select the best classifier implementation amongst a variety of ML libraries (scikit-learn, catboost,
deep learning libraries, etc.);
        • understand and apply principles of generative model design;
        • define and conduct reproducible data-driven experiments.

For further information, including the registration procedure, please refer to the School website:

https://indico.cern.ch/event/1229514
[indico.cern.ch]
Registration is open until the 28th of Feb 2023.

For further questions feel free to contact Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.