The School covers the relatively young area of data analysis and computational research that is emerging in High Energy Physics (HEP). School participants receive a theoretical and practical introduction to this field. A wide range of topics ranging from decision trees to deep learning and hyperparameter optimisation is covered with concrete examples and hands-on tutorials.
In partnership with Yandex School of Data Analysis and EPFL.
2021 Seventh Machine Learning in High Energy Physics Summer School
2020 Sixth Machine Learning in High Energy Physics Summer School
2019 Fifth Machine Learning in High Energy Physics Summer School
2018 Fourth Machine Learning in High Energy Physics Summer School
2017 Third Machine Learning in High Energy Physics Summer School
2016 Second Machine Learning in High Energy Physics Summer School
2015 Summer School on Machine Learning in High Energy Physics
In recent years, the importance of big data analysis in biology has become evident. Computational biology is a new and quickly developing field of data science. The demand for tailored knowledge of bioinformatics in medicine, research, and industry is on the rise. Summer School on Machine Learning in Bioinformatics is fit to provide multidisciplinary knowledge in this area. The School will cover such topics as applied bioinformatics, bioinformatics of DNA, RNA and proteins, elementary genomics, modern methods of data analysis, and molecular biology.
This School aims at spreading and sharing knowledge of state-of-the-art software engineering tools and techniques. Among the speakers of the School are leading programmers and engineers from academia and industry.
The Moscow International School of Physics (MISP) is aimed at advanced students, PhD students and young postdocs working in high-energy physics. The program covers not only experimental and theoretical HEP physics, but also related fields: cosmology, machine learning, and mathematical physics.
International Data Analysis Olympiad — IDAO for short — aims to bridge the gap between the all-increasing complexity of machine learning models and performance bottlenecks of the industry. The participants will strive not only to maximize the quality of their predictions but also to devise resource-efficient algorithms.
Skoltech and HSE invite students in their last year of math- and IT-related bachelor's studies to compete in solving advanced challenges in machine learning as part of the Math of Machine Learning Olympiad (Statistical Learning Theory Olympiad prior to 2021). Winning the Olympiad is equivalent to passing the selection for admission to the HSE University and Skoltech joint master's program Math of Machine Learning.
School participants learn state-of-the-art methods and techniques required for up-to-date research in machine learning. They also have hands-on experience using probabilistic modelling to build neural generative and discriminative models, learn modern stochastic optimization methods and regularization techniques for neural networks, and master the ways to reason about the uncertainty about the weight of the neural networks and their predictions.
In partnership with Samsung AI Center.
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