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109028, Moscow,
11, Pokrovsky boulevard.
Phone: +7 (495) 531-00-00 *27254
Email: computerscience@hse.ru
Kashin B. S., Kosov E., Limonova I. V. et al.
Journal of Complexity. 2022. Vol. 71.
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Journal of Neural Engineering. 2022. Vol. 19. No. 3.
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Proceedings of the American Mathematical Society. 2022. Vol. 150. No. 6. P. 2301-2307.
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Software and Systems Modeling. 2022.
In bk.: ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. Association for Computing Machinery (ACM), 2021. P. 703-715.
The Faculty of Computer Science was created with the goal of becoming one of the world’s leading faculties for developers and researchers in data analysis, machine learning, big data, theoretical computer science, bioinformatics, system and software engineering, system programming, and distributed computing. In cooperation with major companies like Yandex, Sberbank, SAS, Samsung, 1C, and many others, the Faculty provides both deep theoretical knowledge and hands-on practical experience in many branches of contemporary computer science.
On July 15-30, the Seventh International School on Machine Learning in High Energy Physics (MLHEP) organised by the Laboratory of Methods for Big Data Analysis (LAMBDA), Yandex School of Data Analysis and EPFL (Switzerland) took place.
This year's school attracted 121 participants, including representatives of 70 universities and research centres from Russia (Institute of Astronomy of the Russian Academy of Sciences, Joint Institute for Nuclear Research, Moscow Institute for Physics and Technology), Europe (CERN, Oxford and Cambridge Universities) and the USA (Columbia and Princeton University). For the second time, the school was held online.
As usual, the main topics of the school included machine learning, neural networks, deep and distributed learning, generative models and methods, Bayesian methods and optimisation. This year, guest speakers from outside the school complemented the programme with such topics as:
The organisers and participants shared their impressions of the school:
Like last year, the MLHEP2021 summer school was held online. During two weeks a very busy program of lectures and workshops allowed for quite a deep dive into the world of machine learning. I would like to note students' enthusiasm and desire for personal interaction that flourished during social activities, such as games and coffee-breaks in speed-dating format.
Of course, it is impossible to cover everything in a fortnight, but nevertheless, the summer school covered most of the topics connected with machine learning in physics. Each topic was accompanied not only by a lecture but also by a workshop during which algorithms from the lecture were used. This is especially important for beginners in this field.
From the beginner's viewpoint, the summer school provides a large amount of qualitative information in a short time, and that is a plus. It is also important to note the high level of the teachers and the opportunity to talk to them - they were happy to answer any questions.
Experienced researchers were primarily interested in guest lecturers. The format of the school is also very convenient - if you already know the educational part, you can listen to the lecture and talk to the lecturer. Inconvenient time? A recording of the lecture will be available.
As for me, I've refreshed my knowledge and got to know some new algorithms, which I haven't yet encountered in practice, but with a high probability, I will in the near future. Plus it was nice to apply them to the data from the Large Hadron Collider :)
I have been studying machine learning and its applications for several years, yet I did not have a structured knowledge of the field. During my time in this school, I learned several machine learning methods starting from the simplest to the most complicated ones. I also get to see the connection between these methods and how simple techniques add up to elaborate ideas. This school also introduced me to many state-of-the-art neural network designs based on novel ideas that I never imagined possible. I can't wait to get my hands on these sophisticated ideas I learned in this school and apply them in my future work.
We are delighted that the school's programme harmoniously combines talks by machine learning experts on current approaches and methods of data analysis with talks by leading scientists on current challenges and problems in various fields of physics.