Machine Learning and Data Analysis

at ITMO University

Degree :
Subject area:
 Mathematical and natural sciences
 01.00.00 Mathematics and mechanics
 Saint Petersburg , Russia
 2 years
Study type:
 Full time
Language of instruction:
Start date:
Application deadline:
Number of credits:
 School of Translational Information Technologies
Tuition fees per year:
 USD 3600
197101, 49 Kronverksky Pr., St. Petersburg, Russia

School of Translational Information Technologies

Course Summary

The Machine Learning and Data Analysis Master's program is one of few programs in Russia that offers students to acquire education in the area of data science which is rapidly becoming more and more crucial as we now live in the information age.

Students will learn how to use the latest tools and analytical methods to analyze real-world data. It is a great opportunity to study best practices for collection, storage, and retrieval of data, machine learning algorithms and data mining techniques, data visualization and modeling.

The classes are delivered by experienced young scientists and professionals involved in both theoretical and applied machine learning. Besides research activities, the students can take part in hands-on industrial data analysis and machine learning projects, both in leading companies and dynamic high-tech Startups. The students have full access to the server capacities of NVIDIA GeForce GTX video cards - a flagship graphics processor for training deep neural networks. 

The main subjects of study are

  • Modern algorithms and techniques of machine learning and data mining, including probabilistic models and deep learning networks.
  • Mathematical disciplines forming the basis of these algorithms, such as optimization and statistics.
  • Related field of data analysis application: information retrieval, social network analysis and natural language processing.
  • Programming languages and technologies for data mining.
  • Data mining contests devoted to practical application of knowledge and skills to solve Kaggle problems.

The first semester is introductory and includes disciplines to ensure smooth immersion in the basics of machine learning. It allows the students to get a good grip on Python, mathematical statistics, data structures, data analysis tools etc.  The next two semesters focus on disciplines related to specific areas and applications of machine learning and data analysis, such as Deep Learning, Image Analysis, Natural Language Process, Recommender Systems,  Social Network Analysis, etc. And the last semester is fully devoted to research work on the Master's thesis.

Admission Requirements

Bachelor's degree in computer science, mathematics, or engineering disciplines with good/excellent grades. Upper-intermediate level of English.

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