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Third Machine Learning in High Energy Physics Summer School 2017
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Europe/London
Reading University (Reading)
Reading University
Reading
Reading University, Reading, United Kingdom
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Description
The Third Machine Learning summer school organized by Yandex School of Data Analysis, Laboratory of Methods for Big Data Analysis of National Research University Higher School of Economics and Imperial College London will be held in Reading, UK from 17 to 23 July 2017.
The school is intended to cover the relatively young area of data analysis and computational research that has started to emerge in High Energy Physics (HEP). It is known by several names including “Multivariate Analysis”, “Neural Networks”, “Classification/Clusterization techniques”. In more generic terms, these techniques belong to the field of “Machine Learning”, which is an area that is based on research performed in Statistics and has received a lot of attention from the Data Science community.
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 optimization will be covered with concrete examples and hands-on tutorials. A special data-science competition will be organized within the school to allow participants to get better feeling of real-life ML applications scenarios.
Expected number of students for the school is 50-60 people.
Pre-requisites for participation
- Python programming experience (e.g. http://nbviewer.jupyter.org/gist/rpmuller/5920182, https://www.codecademy.com/tracks/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
- optimize 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)
- define & conduct reproducible data-driven experiments
School competition
Details on the school Machine Learning competition are provided on the dedicated page.
Organizers
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Partners
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Sponsors
The school is sponsored by Winton which allows us to provide a subsidy for students who are not able to afford the registration fee out of their own funds. See registration fee page for details.
GPU resources in the cloud are provided by Microsoft Azure.
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Support
