This document provides an overview of the Generative Models School V3.0 Hybrid Edition hosted by Andrey Ustyuzhanin from HSE University from November 23-26, 2021. The school will cover topics ranging from the basics of machine learning to cutting-edge applications and beyond, using examples from high-energy physics. It will focus on formulating physics problems in machine-learning terms, selecting quality criteria, and applying techniques like boosting, bagging, neural networks. Participants will learn to design experiments and optimize models. The school aims to provide an interactive learning experience with lectures, assignments, seminars and social activities.

1. Welcome to
Generative Models!
V3.0 Hybrid Edition
Andrey Ustyuzhanin
HSE University
2021, November 23-26

2. ▶ From basics of Machine Learning to the cutting edge
and beyond
– Mathematical foundations;
– Real life challenges from High-Energy Physics;
– Industry-related illustrations and examples;
– Socialization and Networking.
▶ We love ML and Particle Physics ;-)
School Idea
Andrey Ustyuzhanin, NRU HSE

3. Andrey Ustyuzhanin, NRU HSE

4. ▶ formulate a HEP-related problem in ML-friendly terms;
▶ select quality criteria for a given problem and express it in computable form;
▶ understand and apply principles of widely-used classification models (e.g. boosting,
bagging, BDT, neural networks, etc) to practical cases;
▶ design the procedure for choosing the best classifier implementation amongst a variety of
ML libraries (scikit-learn, xgboost, etc.) and deep learning architectures;
▶ understand and apply principles of generative model design;
▶ optimize features and parameters of a given model in efficient way under given
restrictions;
▶ understand optimization algorithm design and apply those to a variety of real-life
problems;
▶ define and conduct reproducible data-driven experiments.
Learning objectives
Andrey Ustyuzhanin, NRU HSE

5. 5
Global Search Engines
Andrey Ustyuzhanin

6. ▶ Established in March 2014,
▶ Active support from industry: Yandex (more than 50 lecturers/seminarists are
Yandex employees), Kaspersky, WorldQuant, Huawei, SAS, JetBrains, and other
▶ Goal: enter the world’s top 30 Computer Science faculties by 2022
– big data storing and processing,
– software development and system programming,
– machine learning
▶ ~400 students enter bachelor program every year
▶ ~100 graduate master’s program every year
▶ Member of LHCb collaboration since June 2018
▶ Collaborates with many European and US universities
HSE Faculty of Computer Science
Andrey Ustyuzhanin, NRU HSE

7. ▶ Development and application of ML methods and tools to
Natural science challenges
▶ Close partnership with Yandex School of Data Analysis
▶ Collaborates with LHCb, SHiP, OPERA, NEWSdm experiments
▶ Research Project examples:
– Storage/Speed optimization for LHCb triggers;
– Particle Identification algorithms;
– Optimization of detector;
– Event simulation speed-up.
▶ Co-organization of ML challenges: Flavours of Physics,
TrackML
▶ Open for interns, graduate students and post doc candidates!
Laboratory of Methods for Big Data Analysis
Andrey Ustyuzhanin, NRU HSE
hse_lambda

8. ▶ 2015 – Academic University, Saint Petersburg, Russia
▶ 2016 – Lund University, Sweden
▶ 2017 – Imperial College London, Reading, UK
▶ 2018 – Oxford, UK
▶ 2019 – Hamburg, Germany
▶ 2020 – EPFL + Online
▶ 2021 – EPFL + Online
MLHEP - iterative education experiment
Andrey Ustyuzhanin, NRU HSE

9. MLHEP teaching team
Andrey Ustyuzhanin

10. ▶ Teaching assistants
– Ekaterina Trofimova
– Abdalaziz Al-Maeeni
– Alexander Rogachev
– Sergei Popov
– Tigran Ramazyan
– Alexander Inyukhin
▶ Technical support
– Dmitry Maevskiy
▶ Overall support
– Natalia Talaikova
Assistance
Andrey Ustyuzhanin, NRU HSE

11. ▶ Click “Open assignment”
▶ Open Jupyter notebook
Start notebook
Andrey Ustyuzhanin, NRU HSE

12. ▶ Main activities:
– lectures, seminars, competitions
▶ Most of the lectures are pre-recorded, Q&A are part of the
seminar
▶ Schedule
– https://indico.cern.ch/event/1025052/timetable/ , CEST Time Zone
▶ All classes are going to be recorded and stored for the
participants at ALEMIRA platform
Recap
Andrey Ustyuzhanin

13. ▶ ECTS credits (MLHEP as EPFL official course)
– 4 Credits
▶ Evaluation metrics:
– Quizzes – 1 score point each quiz
– Seminar tasks – 6 score points if all tasks performed correctly
– Data challenge & presentations – wait for Nikita’s presentations
– Watching videos and interactive contents also adds to the metrics ~0.1 of points
▶ Evaluation criteria
– 60% + of top score
– Will be calculated upon the end of the school (~2nd
of August)
▶ Certification
– Certificate of excellence will be awarded with the same criteria as for ECTS credits
Qualification
Andrey Ustyuzhanin

14. ▶ Social Activities, past years examples
– Zoom lunch with lecturers
– TED-style talks
– Mafia-style games
– 3D-shooter teamplay online
▶ This year ideas:
– Random coffee, around 26th
of July
– “Among Us” tournament
▶ Communication channels:
– Zoom for interactive classes (seminars, live talks)
– MS teams for Q&A, technical assistance, social activities
Social activities
Andrey Ustyuzhanin

15. School Sponsors

16. Thank you!
austyuzhanin@hse.ru
anaderiRu
hse_lambda
Andrey Ustyuzhanin
Andrey Ustyuzhanin, NRU HSE