Summer School on Machine Learning for High Energy Physics 2018
Dear Colleagues,
This is the first announcement of the Summer School on Machine Learning for High Energy Physics 2018, to be held in Oxford, UK, August 6-12 2018. The school is organised by National Research University HSE, Yandex School of Data Analysis and University of Oxford.
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 organised.
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;
- optimise 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);
- understand and apply principles of generative model design;
- define and conduct reproducible data-driven experiments.
For further information, including registration procedure, please refer to the Summer School website:
or contact mlhep2018@yandex.ru
Early registration deadline is 2nd of April, 2018.