The amount of accumulated data today opens the door for rigorous analysis and the development of predictive models that can be used for economic benefit. Students of the Master’s programme in Machine Learning and High-load Systems gain a deep understanding of how to collect, store, and process data in addition to learning how to construct predictive models and assess their quality.
Many courses focus on distributed systems and methods of rapidly processing large amounts of information. Distributed systems allow for the efficient use of processing power by parallelizing and distributing a computation load onto multiple independent processors; they also perform calculations on clusters using data processing algorithms.
Students of the programme learn about the construction of high-load and fail-safe services, which enables them to work in all stages of development processes once they graduate.
The programme’s curriculum is practice-oriented, so that graduates are able to manage all stages of model development: from identifying a data analysis problem to collecting and processing data, from teaching the algorithm and evaluating its quality to putting the model into use.
Who should apply
Applicants who know the fundamentals of advanced mathematics and have programming experience but lack training in machine learning are especially encouraged to apply.
This programme is aimed at students who want to delve into machine learning, gain a deep understanding of ML algorithm theory, and acquire practical experience solving data analysis problems.
Applicants should have a firm grasp of the fundamentals of advanced mathematics (mathematical analysis, linear algebra, discrete mathematics, probability theory), have previous experience with programming (acquired during undergraduate studies or other equivalent experience), and know the essentials of algorithm theory.
Advantages of the programme
- Online format – study from anywhere in the world.
- Interactive approach: the majority of courses include Zoom seminars with professors. Seminars allow you to understand the topics of the course better, ask questions, and productively communicate with classmates.
- A flexible curriculum allows students to choose the most useful courses and design an individual learning track.
- Project-oriented programme – students put their acquired skills into use during the programme, solving real-life problems and participating in project and research seminars.
- A large number of courses is devoted to industrial development and technologies for the construction of distributed and high-load systems.
Applicants must hold a Bachelor’s or a Specialist’s degree in any field of study.
To enroll, one needs to successfully pass the entrance exams: an online test in mathematics and programming with proctoring and an online interview.
All classes in the programme are held online. Formats:
- synchronous – Zoom lectures and seminars
- blended – an online course and additional Zoom seminars
During the first term, students take courses in mathematics for data analysis, programming language (Python, C++ elective), and algorithm theory, as well as industrial development. In subsequent modules, students delve into machine learning, study development and distributed systems, and choose elective courses and projects.
At the end of the first year, students prepare a final project in which they apply machine learning to an actual problem.
At the end of the second year, students prepare and defend a thesis.
The curriculum consists of twenty-six courses. Every course includes seminars held over Zoom and online consultations to ensure that students maintain close contact with their professors and receive necessary support.
After the programme
Graduates of the programme will be competitive candidates for Data Scientist or Machine Learning Engineer positions. Those pursuing an academic career in data science may apply to the HSE University doctoral school.
We are a small community of people who share a passion for innovative technology and research. In the programme, you will communicate with your classmates in messenger applications and meet online to work together on individual and group tasks, and discuss your ideas and questions in seminars. You will be able to attend online career events to meet leading IT company experts, as well as webinars where industry experts share their experience working in the field.
Elena Kantonistova – Candidate of Physical-Mathematical Sciences; Associate Professor of the Faculty of Computer Sciences, HSE University; Academic Supervisor of Machine Learning and High-load Systems programme; Data Scientist at UCGroup.
Vladimir Podolskii – Candidate of Physical-Mathematical Sciences; Head of Big Data and Information Retrieval School at the Faculty of Computer Sciences, HSE University; Senior Research Fellow of the Laboratory of Theoretical Computer Science; Academic Supervisor of Master of Data Science programme.
Evgeny Sokolov - Academic Supervisor of Applied Mathematics and Computer Science programme, Faculty of Computer Sciences, HSE University; Deputy Head of Big Data and Information Retrieval School.
You will learn how ML algorithms work, how to apply your theoretical knowledge, get experience with industrial development, and be able to complete every step of a data analysis project, from data search and processing to constructing,from validating an ML model to its implementation.
Yes, so long as you can devote twenty to thirty hours per week to your studies
Our online programme is a full-fledged master’s programme, which grants an official state diploma toits graduates. Every class will have Zoom seminars to supplement Zoom lectures or online courses. You’ll be able to communicate directly with professors of every course.
Yes, you can receive a discount according to your entrance exam results
Applicants should have Bachelor’s-level knowledge of higher mathematics and a basic knowledge of some programming language (writing and interpreting the code with conditional statements, cycles, procedures, and other basic structures).