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 expected number of students for the school is 60. The school is aimed at PhD students and postdoctoral researchers, but also open to masters students.
Pre-requisites for participation
- Python programming experience (e.g. http://nbviewer.jupyter.org/gist/rpmuller/5920182, https://www.codecademy.com/learn/learn-python)
- interest and/or background in HEP
- laptop with WiFi connectivity
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.