Annual Machine Learning Summer School Co-Organized by Yandex Heads to Germany

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The annual Machine Learning in High Energy Physics summer school, co-organized by the Yandex School of Data Analysis and the Laboratory of Methods for Big Data Analysis at Moscow’s Higher School of Economics (HSE), is this year being staged in Hamburg, Germany. The DESY research center will host the fifth MLHEP summer school from July 1st to July 10th.  The program will welcome 71 postgraduates and postdoctoral researchers from 17 countries, with most coming from the EU, the United States, and Russia. Over ten days, students at the summer school will have both a theoretical and practical introduction to machine learning in particle physics.

Yandex is continually looking for ways to advance machine learning for their users and the greater AI community, and one way of doing that is to encourage data science learning.  The company's education initiatives offer opportunities for a broad range of learners, from those interested in online courses to professionals looking for career advancement in computer science.  Many of Yandex's education programs stem from their collaborations with higher education institutions, which enable them to work with the brightest scientific minds to teach diverse topics in machine learning. 

The annual Machine Learning in High Energy Physics summer school which Yandex helps organize is an excellent example of one of their academic collaborations.  The Yandex School of Data Analysis and the Laboratory of Methods for Big Data Analysis at Moscow’s Higher School of Economics (HSE) have annually staged the summer school since 2015.  Each year, YSDA works with a different scientific partner in Europe to host the summer program. This year, the DESY research center in Hamburg, Germany, will host the fifth MLHEP summer school from July 1st to July 10th.  The program will welcome 71 postgraduates and postdoctoral researchers from 17 countries, with most coming from the EU, the United States, and Russia.  

The MLHEP summer school focuses on the emerging fields of data analysis and computational research in High Energy Physics (HEP), also known as particle physics.  Machine learning helps solve essential problems in HEP that range from online data filtering and reconstruction to offline data analysis. Over ten days, students at the summer school will have both a theoretical and practical introduction to machine learning in HEP, covering topics from decision trees to deep learning and hyperparameter optimisation.  Students will have the opportunity to apply what they learn with concrete examples and hands-on tutorials.

Participants in previous years have come from all over the world with diverse backgrounds to enhance their machine learning skills.  

“During the MLHEP school, I widened my understanding of machine learning methods,” says Mikkel Bjorn, a DPhil student in Elementary Particle Physics at the University of Oxford.  “I learned new ideas about where the techniques we studied can be useful in the work of myself and my group.” 

Alexey Kharlamov, a recent graduate of HSE, adds that “Most of all I liked the atmosphere of the program, which cultivated an interest in machine learning as a result of working with both motivated students and excellent teachers who love their subject.  In such an environment, it’s exciting to develop your data science skills.”

The MLHEP summer program emphasizes both theoretical knowledge and practical application to ensure students come away with applicable skills.  YSDA organizes a related machine learning competition that spans two to three months to provide a continued opportunity for students to apply their knowledge.  The competition is inspired by Yandex’s long-standing relationship with CERN, where researchers from Yandex have been working with physicists to solve issues related to matter and energy.  In particular, students will be creating solutions related to the Large Hadron Collider beauty experiment at CERN. The competition will require students to process particle information using modelling techniques.  The two-part contest will be similar to Kaggle machine learning competitions and take place in a co-learning environment, encouraging students to work together to solve challenges.

Lecturers from the Faculty of Computer Science at HSE, a department Yandex co-founded, will teach most of the sessions.  As Yandex is always eager to promote an atmosphere of collaboration in their education initiatives, the HSE specialists will be joined by several guest lecturers from Facebook, Oracle, Caltech, and more, who will be teaching sessions on causal inference, probabilistic programming, and other machine learning topics.

The MLHEP summer school is yet another exciting opportunity for Yandex to collaborate with academia and encourage data science learning.  For more information about the program, please visit the website and follow @yandexcom on Twitter to get updates during the summer school!

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Melissa McDonald
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