Summer School on Machine Learning for High Energy Physics 2017

This is the first announcement of the Summer School on Machine Learning for High Energy Physics 2017, to be held in Reading, UK, July 17-23 2017. The school is organised by Yandex School of Data Analysis, Imperial College London and Higher School of Economics.

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

Additionally, the school will include 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 problem in machine learning friendly terms
– select quality criteria for given problem
– understand and apply principles of widely-used classification models (e.g. boosting, bagging, BDT, neural networks, etc) to practical cases
– optimize features and parameters of given model in efficient way under given restrictions
– select the best classifier implementation amongst variety of ML libraries (scikit-learn, xgboost, deep learning libraries, etc)
– define and conduct reproducible data-driven experiments

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

Early registration deadline is 30 April, 2017.