The Spring School on Machine Learning for High Energy Physics 2023 will be held in Erice, 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.
Registration is open until the 28th of Feb 2023.