School of Huawei Advanced Research Education at Lomonosov Moscow State University

The schedule has been updated for 2020-2021 academic year. General schedule grid.
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About the SHARE MSU program

School of Huawei Advanced Research Education at Lomonosov Moscow State University, or SHARE MSU, was organized in September 2019 as a result of the synergy of one of the leading multinational corporations in research and development (R&D) - Huawei - and the largest University in Russia - Lomonosov Moscow State University.
The goals of this school are: At the moment, the program is two-year and consists of more than 10 half-year (semester) courses (some courses last two semesters), taught by teachers of the Lomonosov Moscow State University in collaboration with the staff of the Huawei Moscow Research Center. Courses are combined into two specializations: "Machine Learning and Computer Vision" and "Big Data and Information Theory".
Duration of training
2 years
Study load
On average, 2 lecture courses per semester + possible additional seminars
Enrollment
4-6 year students, masters, PhD students and graduates of the following faculties of Lomonosov Moscow State University: mechanics and mathematics, computational mathematics and cybernetics, physics, space research
Cost of education
Free
Certification
SHARE MSU graduates receive a Certificate of Additional Education from Lomonosov Moscow State University

News

2021, February 8
The schedule has been updated for 2020-2021 academic year.
General schedule grid.
2020, September 30
SHARE MSU 2020 was re-presented online at 18:00.
Additional presentation of the 2020 program. Video conference recording.
2020, September 28
Online re-submission of the program - 30 september at 18:00. You can fill out an application for participation in the program by link. To participate in an organizational meeting, you must fill out a form, since a link to an online meeting will be sent to the email address specified in the form.
Invitation poster
Poster
2020, September 23
SHARE MSU 2020 program was presented online at 18:00.
Presentation of the 2020 program. Video conference recording.
2020, September 17
Online program presentation - 23 september at 18:00. You can fill out an application for participation in the program by link. To participate in an organizational meeting, you must fill out a form, since a link to an online meeting will be sent to the email address specified in the form.
Invitation poster
Poster
2020, June 26
The 6 best students among those with the highest scores were selected as interns to the Intelligent Systems and Data Science Laboratory of the Huawei Moscow Research Center.
2020, May 29
Additional lecture on the course "Practical issues of machine learning" with presentations of the best works at the kaggle-competition.
2020, March 17
Lectures of all courses go online in connection because of the COVID-19 pandemics.
2020, February 18
Specialization "Computer Vision and Machine Learning" began the second semester with a lecture of the course "Practical Issues of Machine Learning" in the auditorium 1205 of the main building MSU.
2019, September 28
Specialization "Computer Vision and Machine Learning" started with the first lecture of the course "Mathematical Methods of Digital Signal Processing" in auditorium 1503 of the main building MSU.
2019, September 19
The SHARE MSU 2019 program was presented at 17:00 in auditorium 1624 in the main building of Moscow State University.
Presentation program-2019.
Invitation poster
Photo from the meeting
Poster
2019, September 15
The registration page for the SHARE MSU program has been created at timepad.

Schedule

General schedule grid

2020-2021 academic year

2021, Spring semester

Specialization "Machine Learning and Computer Vision"
Year 1, Course "Mathematical Methods of Digital Image Processing"
Place: online
Time: Saturday, 16:45
Start: 13.02.2021
Year 1, Course "Practical issues of modern computer vision"
Place: online
Time: Tuesday, 16:45
Start: 16.02.2021
Year 1, Seminar "Applied Computer Vision"
Place: online
Time: Tuesday, 18:15
Start: 16.02.2021
Year 2, Course "Introduction to the Theory of Neural Networks II"
Place: online
Time: Wednesday, 15:00
Start: 10.02.2021
Specialization "Big Data and Information Theory"
Year 1, Course "Application of graph theory to the synthesis of LSI II"
Place: online
Time: Friday, 15:00
Start: 12.02.2021
Year 1, Course "Development of big-data-applications on Apache Spark"
Place: online
Time: Friday, 18:30
Start: 12.02.2021

2020, Autumn semester

Specialization "Machine Learning and Computer Vision"
Year 1, Course "Mathematical Methods of Digital Signal Processing"
Place: online
Time: Saturday, 16:45
Start: 10.10.2020
Year 1, Course "Practical issues in machine learning"
Place: online
Time: Tuesday, 18:00
Start: 06.10.2020
Year 1, Seminar "Applied machine learning"
Place: online
Time: Tuesday, 19:30
Start: 06.10.2020
Year 1, Seminar "The Python Programming Language for Researcher"
Place: online
Time: Friday, 18:00
Start: 02.10.2020
Year 2, Course "Introduction to the theory of neural networks I"
Place: online
Time: Wednesday, 15:00
Start: 07.10.2020
Specialization "Big Data and Information Theory"
Year 1, Course "Application of graph theory to the synthesis of LSI I"
Place: online
Time: Friday, 15:00
Start: 09.10.2020
Year 1, Course "Functional Programming in Scala / Haskell"
Place: online
Time: Friday, 18:00
Start: 09.10.2020
Year 2, Course "Software development environments for VLSI"
Place: online
Time: Friday, 13:00
Start: 09.10.2020
Year 2, Course "Introduction to the theory of error-correcting coding"
Place: online
Time: Tuesday, 18:30
Start: 06.10.2020

2019-2020 academic year

2020, Spring semester
Specialization "Machine Learning and Computer Vision"
Year 1, Course "Mathematical Methods of Digital Image Processing"
Place: auditorium 1205, main building MSU
Time: Saturday, 16:45
Year 1, Course "Practical issues in machine learning"
Place: auditorium 1205, main building MSU
Time: Tuesday, 18:30
Specialization "Big Data and Information Theory"
Year 1, Course "Application of graph theory to the synthesis of LSI II"
Year 1, Course "Development of big-data-applications on Apache Spark"
2019, Autumn semester
Specialization "Machine Learning and Computer Vision"
Year 1, Course "Mathematical Methods of Digital Signal Processing"
Place: auditorium 1503, main building MSU
Time: Saturday, 16:45
Year 1, Course "Practical issues of modern computer vision"
Place: auditorium 1205, main building MSU
Time: Tuesday, 18:30
Specialization "Big Data and Information Theory"
Year 1, Course "Application of graph theory to the synthesis of LSI I"
Year 1, Course "Functional Programming in Scala / Haskell"

Courses SHARE MSU

Specialization "Machine Learning and Computer Vision"
Course "Mathematical Methods of Digital Signal Processing"
Duration: 1 semester
Teachers: Ph.D., senior researcher Mazurenko Ivan Leonidovich, post-graduate student Dzabraev Maxim Dmitrievich
About the course
  • Fundamentals of the theory of digital signal and image processing
  • Basic methods of digital processing of signals and images in the time / space and frequency domains
  • The main classical tasks of digital signal and image processing
  • Examples of applied tasks
  • Libraries of digital signal and image processing in Matlab/Octave
  • OpenCV library
Course "Mathematical Methods of Digital Image Processing"
Duration: 1 semester
Teachers: Ph.D., senior researcher Mazurenko Ivan Leonidovich, post-graduate student Dzabraev Maxim Dmitrievich
About the course
  • Fundamentals of the theory of digital signal and image processing
  • Basic methods of digital processing of signals and images in the time / space and frequency domains
  • The main classical tasks of digital signal and image processing
  • Examples of applied tasks
  • Libraries of digital signal and image processing in Matlab/Octave
  • OpenCV library
Course "Practical Issues in Machine Learning"
Duration: 1 semester
Teachers: Doctor of Physical and Mathematical Sciences, prof. Babin Dmitry Nikolaevich, Ph.D. Ivanov Ilya Evgenievich, Ph.D. Petiushko Aleksandr Aleksandrovich
About the course
  • Basic Machine Learning Tasks and Quality Metrics (ROC-Curve)
  • Classification methods
  • Regression methods
  • Algorithms compositions
Course "Practical issues of modern computer vision"
Duration: 1 semester
Teachers: Doctor of Physical and Mathematical Sciences, prof. Babin Dmitry Nikolaevich, Ph.D. Ivanov Ilya Evgenievich, Ph.D. Petiushko Aleksandr Aleksandrovich
About the course
  • The main tasks of computer vision (classification, detection, segmentation, image enhancement)
  • The history of applying convolutional neural networks to images
  • Generative models
  • Examples of applied tasks
Course "Introduction to the theory of neural networks"
Duration: 2 semesters
Teachers: Ph.D., Assoc. Chasovskikh Anatoly Aleksandrovich, Ph.D. with. Polovnikov Vladimir Sergeevich, post-graduate student Ronzhin Dmitry Vladimirovich
About the course
  • Basic neural network architectures and their functional properties
  • Optimization of the complexity and speed of neural networks
  • Justification of the procedure for training neural networks of direct propagation
  • Features of the architecture and training method of recurrent neural networks
  • Open image databases
  • Convolutional neural networks. Tasks of classification, detection, segmentation of images
  • Recurrent neural networks. Memory Simulation and Signal Sequence Processing
Seminar "Applied Machine Learning"
Duration: 1 semester
Teachers: Doctor of Physical and Mathematical Sciences, prof. Babin Dmitry Nikolaevich, Ph.D. Ivanov Ilya Evgenievich, Ph.D. Petiushko Aleksandr Aleksandrovich
About the seminar
  • Data manipulation and machine learning frameworks in Python
  • Machine learning competition
Seminar "Applied Computer Vision"
Duration: 1 semester
Teachers: Doctor of Physical and Mathematical Sciences, prof. Babin Dmitry Nikolaevich, Ph.D. Ivanov Ilya Evgenievich, Ph.D. Petiushko Aleksandr Aleksandrovich
About the seminar
  • Image and neural network frameworks in Python
  • Computer vision competition
Seminar "Python Programming Language for Researcher"
Duration: 1 semester
Teachers: Doctor of Physical and Mathematical Sciences, prof. Babin Dmitry Nikolaevich, Ph.D. Ivanov Ilya Evgenievich, Ph.D. Petiushko Aleksandr Aleksandrovich, Ph.D. Ivanyuta Andrey Sergeevich, Korvyakov Vladimir Petrovich
About the seminar
  • Python basics
  • Specialized libraries (Numpy, Pandas, scikit-learn)
  • Data visualization (Matplotlib, openCV, scikit-image)
Specialization "Big Data and Information Theory"
Course "Application of graph theory to the synthesis of LSI"
Duration: 2 semesters
Teachers: Ph.D., Assoc. Chasovskikh Anatoly Aleksandrovich, Ph.D. with. Polovnikov Vladimir Sergeevich, post-graduate student Ronzhin Dmitry Vladimirovich
About the course
  • Mathematical model of LSI design based on the technology of their synthesis
  • Planar graphs. The Pontryagin - Kuratovsky theorem. Algorithm for stacking planar graphs, characteristics of nonplanar graphs
  • Minimal Rectangular Steiner Trees, Exact and Approximate Solutions
  • Graph coloring theorems, realization of power sequences by graphs
  • Flat circuits, an estimate of the complexity of arithmetic flat circuits
  • Element placement heuristic algorithms
  • Optimizing wire routing
  • Synthesis of specialized circuits: sorters, arithmetic circuits, etc.
Course "Software development environments for VLSI"
Duration: 1 semester
Teachers: Doctor of Physical and Mathematical Sciences, prof. Hasanov Elyar Eldarovich, Ph.D. n., m. n. with. Shutkin Yuri Sergeevich
About the course
  • Chip Design Basics
  • Development of simulation and testing tools for hardware designs
  • Analyzing the complexity of hardware designs
  • Wireless transmission of information
  • Storage systems
  • Error-correction codes
Course "Introduction to the theory of error-correcting coding"
Duration: 1 semester
Teachers: Ph.D., Assoc. Panteleev Pavel Anatolievich
About the course
  • Classical algebraic codes (BCH, Reed-Solomon, Reed-Muller codes)
  • Modern code designs (low density, convolutional, polar codes)
  • Practical aspects of implementing encoders / decoders
  • Distributed storage codes
  • Quantum codes
Course "Functional Programming in Scala / Haskell"
Duration: 1 semester
Teachers: Ph.D., junior researcher Sokolov Andrey Pavlovich, Moiseev Stanislav Vladimirovich
About the course
  • Typed Lambda Calculus, Hindley-Milner Type System
  • Curry-Howard correspondence between computer programs and mathematical proofs
  • Brouwer-Heyting-Kolmogorov Interpretations of Intuitionistic Logic
  • Learning functional programming and related concepts (functions, functors, applicative functors, monads, monad-transformers, etc.)
  • Functional data structures and algorithms
Course "Apache Spark Big Data Application Development"
Duration: 1 semester
Teachers: Ph.D., junior researcher Sokolov Andrey Pavlovich, Moiseev Stanislav Vladimirovich
About the course
  • Distributed data storage and processing systems
  • Design and analysis of distributed algorithms
  • Framework Apache Spark
  • Statistical data analysis
  • Working with tables
  • Tasks on Graphs

Internships

To support the best students in the SHARE MSU program, an internship program has been opened at the Intelligent Systems and Data Science Laboratory of the Huawei Moscow Research Center, where interns can gain invaluable real-world industrial experience in one of the leading multinational corporations in the field of R&D, solving interesting and non-standard problems at the edge of science and technology.
To apply for an internship, you should send an e-mail to SHARE@intsys.msu.ru with "Topic" line [Internship] your resume (CV), in which it is desirable to present:
  • Your full name, contact information (e-mail and phone number);
  • A photo;
  • Information about your education;
  • Additional relevant courses listened to;
  • Info about participation in school and / or student olympiads and competitions;
  • Implemented projects (for example, on github);
  • Work experience and / or internship;
  • Your strong professional and personal qualities;
  • Any other information that you deem necessary.

Intelligent Systems and Data Science Laboratory

Intelligent Systems and Data Science Technology Center was established within the framework of the Huawei Moscow Research Center back in September 2014 under the leadership of Ph.D., senior researcher Mazurenko Ivan Leonidovich. Research areas of the Laboratory are listed below:
  • Distributed storage systems and big data processing;
  • Working with huge structured data sets;
  • Machine learning algorithms;
  • All classic computer vision problems;
  • Error-correcting codes (including for quantum simulators);
  • Optimizing Next Generation Chips;
  • Fundamental Problems of Artificial Intelligence.
In the field of fundamental problems of artificial intelligence, the Laboratory cooperates with such scientific institutions as:
  • Lomonosov Moscow State University;
  • SPbSU;
  • SkolTech;
  • MIPT.
ISDS Collaboration

Contacts

Program SHARE MSU
E-mail: SHARE@intsys.msu.ru
Telegram-channel: https://t.me/joinchat/AAAAAE_r4XKzEDaUKy1FwA
Program coordinator: Petiushko Aleksandr Aleksandrovich (e-mail: petiushko.aleksandr@intsys.msu.ru)
Executive Secretary: Kochetkova Tatiana Yurievna (e-mail: kochetkova.tatiana@huawei.com, tel.: +7 (925) 597-69-19)
Direction "Machine Learning and Computer Vision"
Telegram-channel: https://t.me/joinchat/AAAAAEUmx5cJLOdLXsOt8g
Direction "Big Data and Information Theory"
Telegram-channel: https://t.me/joinchat/AAAAAFLAuQvYDpobU3x4WQ
Courses "Mathematical Methods for Digital Signal and Image Processing"
Telegram-channel: https://t.me/joinchat/AAAAAEz_z-20xz0UUF6wQw
Courses "Introduction to the theory of neural networks"
Telegram-channel: https://t.me/joinchat/AAAAAFCZElJDhPdA_0XV7g
Course "Functional Programming in Scala / Haskell"
Telegram-channel: https://t.me/joinchat/AAAAAFjy2F6xZwhLgXxr9A
Course "Introduction to the theory of error-correcting coding"
Telegram-channel: https://t.me/joinchat/GZYNExrEjIXscy0RPHI8rg
Department of Master's and Additional Education, Faculty of Mechanics and Mathematics, Lomonosov Moscow State University:
Director of Department
Deputy Dean for Academic Affairs Popelensky Mikhail Yurievich
Auditorium
1507a main building MSU
Branch website
www.math.msu.ru
Telephone
+7 (495) 939-32-11