Enroll in the course
Machine Learning for Physicists
For students who want to use machine learning in academic and research fields
Course Registration Deadline: September 20, 2021
Join the webinar to learn more: June 9 at 15:00 MSK
With the IT sector expanding rapidly around the world, there has never been a better time to take a machine learning course. The Machine Learning for Physicists course will provide you with the knowledge and practical skills you need to further your career or research activities.
The course is available for undergraduate students gearing up for their 4th year of study, designed for programmer beginners. You will gain introductory knowledge that will help you grasp common sense concepts and advanced techniques on machine learning.
You will receive a theoretical and practical introduction to this new field and will be able to apply acquired knowledge to solve your own problems. Topics will range from decision trees to deep learning and simulation-based inference and will be covered with concrete examples and hands-on tutorials.
Get ready to gain key insights on the following modules: Intro into Deep Learning, Supervised Deep Learning Models, Unsupervised Deep Learning Models, Advanced Learning.
Join the Webinar
Join us June 9 at 15:00 MSK for an online webinar and discover why Machine Learning for Physicists is a good fit for you.
Meet the speakers:
• Prof. Valery Kiselev, Director of Phystech-cluster of Academic and Scientific Career of Landau Phystech School of Physics and Research – Moscow Institute of Physics and Technology
• Ilya Shimchik, Acronis SIT Autonomous Team Principal
• Prof. Andrey Ustyuzhanin, AI/MI expert consultant at SIT, the director of the Laboratory of Methods for Big Data Analysis at the Higher School of Economics
This course, designed for those with little to no programming experience, will provide students with a non-conventional introduction into deep machine learning, understanding of both supervised and unsupervised deep machine learning models and advanced machine learning over the course of 2 semesters (120 hours of lessons).
Get acquainted with the main machine learning algorithms used in today’s academic and business research across different fields: deep learning, convolutional neural networks, computer vision, time series, generative networks, autoencoders, neurodiffs, and optimization methods.
MIPT students from the Department of General and Applied Physics can choose one course between: “Machine Learning for Physicists“, “Oscillations and Waves” and “Electronic methods of physical research".
Students from other Phystech Schools can include “Machine Learning for Physicists“ to their individual plan.
The course is designed for students entering their 4th year of study.
About SIT AlemiraSIT Alemira provides a complete digital ecosystem for education and learning. Through an AI-powered authoring platform, SIT Alemira supports universities like MIPT to create adaptive content for a more collaborative research design approach. Advised by leading education experts, SIT Alemira leverages Active Learning technologies to deliver hands-on learning experiences and complex results, enhancing the online learning experience.
Schaffhausen Institute of Technology (SIT), located in Schaffhausen, Switzerland, is an international institution founded by entrepreneurs, led by scientists and advanced by world-class researchers. Interdisciplinary by design, SIT comprises a unique ecosystem, including that of SIT Alemira, where the world’s leading experts in Computer Science, Physics and Business come together to find innovative solutions to global challenges through transformative technological advances.