The course introduces students to a wide range of modern machine learning methods, the main types of problems that can be solved with their help, data preparation and processing techniques, and the modern tools used for machine learning. The course consists of lectures and practical sessions and ensures that students not only acquire theoretical knowledge but also develop practical skills in solving data processing tasks. The focus of the course is on solving problem types that arise when working with data in physics, taking into account their characteristic properties and features.
Upon completing the course, the student will gain general knowledge about working with data, as well as about the machine learning methods covered, their theoretical foundations, areas of application, and specifics of practical use. They will also acquire practical skills in working with data using certain computer programs and software environments where the discussed methods are implemented.
The course program includes 16 sessions: 11 lectures, 5 practical sessions, plus a Kaggle competition
Requirements for students:
Teachers:
Ismail M. Gadziev
Education
Faculty of Physics
MSU
Areas of Expertise
Machine learning, statistical data analysis
Sergey A. Dolenko
Education
Faculty of Physics
MSU
Areas of Expertise
Machine learning in natural sciences and in solving engineering problems; artificial neural networks; genetic algorithms; spectroscopy
Alexander O. Ephitorov
Education
Faculty of Physics
MSU
Areas of Expertise
Conditioned example generation using deep networks, machine learning in physical dimensions
Igor V. Isaev
Education
Faculty of Physics, Department of Physics of Nanosystems
MSU
Areas of Expertise
Application of machine learning methods to solve inverse problems of experimental data processing
Vladimir R. Shirokiy
Education
Faculty of Chemistry
MSU
Areas of Expertise
Machine learning, data engineering
Занятия проводятся на Физическом факультете МГУ
В программе курса 16 занятий: 11 лекций, 5 практических занятий + соревнование на Kaggle
Старт курса: с 13 сентября по вторникам 17:30-19:30
Требования к студентам:
Email курса: mlphysics2022@mail.ru
Набор на курс 2022 года закрыт
FRIDAYS
09:00–10:30 — period 1
10:40–12:10 — period 2
12:10–13.00 — break
13:00–14:30 — period 3
14:40–16:10 — period 4
Instructor: A. Ganichev
THURSDAYS
09:00–10:30 — period 1
10:40–12:10 — period 2
12:10–13.00 — break
13:00–14:30 — period 3
14:40–16:10 — period 4
Instructor: A. Marakulin
MONDAYS
09:00–10:30 — period 1
10:40–12:10 — period 2
12:10–13.00 — break
13:00–14:30 — period 3
14:40–16:10 — period 4
Instructor: D. Penzar
SATURDAYS
09:00–10:30 — period 1
10:40–12:10 — period 2
12:10–13.00 — break
13:00–14:30 — period 3
14:40–16:10 — period 4
Instructor: I. Konyushok