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Digital Culture Master’s courses

Section Contents
Module description 

The lectures in the Digital Culture module equip students with the competencies necessary to use the information and communication technologies that help us navigate the digital environment, as well as interact with society and find digital solutions for professional tasks. 

Here, you will learn to formulate problems, choose the correct tools and algorithms for data analysis, and use them. You will also be able to interpret the results that you get. 

How to sign up for the course

Step 1. Activate your account on the Open Education national platform. An activation link will be sent to the email address listed on your personal ISU page on the day the course starts. 

Step 2. Open the course materials and start learning. 

A more detailed instruction (in Russian)

Contact us

Address:

  • 49 Kronverksky Avenue, bldg. A, room 420
  • 14 Birzhevaya Line, rooms 446-447

Phone: 8 (812) 607-04-64

E-mail: dc@itmo.ru

Courses

Data Processing and Analysis (in Russian)

 More information you can find here Обработка и анализ данных

Data Processing and Analysis

The discipline “Data processing and analysis” is aimed at teaching the students the culture of data processing starting from the data preprocessing methods to the most modern data processing and analysis methods. The discipline consists of two courses.

The course “Data Storage and Processing” is mandatory for all students. This course discusses the problems of data pre-processing, descriptive statistics and data visualization, time series, as well as data storage and analysis by means of relational and NoSQL DBMS. After mastering this course students should take one of the following courses:

  • The Elements of Statistical Data Processing”, designed for the students not familiar with the main concepts of the probability theory or statistical data analysis. The course examines various questions, such as distribution types, sample characteristics, point estimates, confidence interval construction principles, as well as statistical hypothesis testing. 
  • “Introduction to Machine Learning”. This course introduces different types of machine learning, shows how to solve applied tasks by means of machine learning methods. The course focuses on regression task, as well as on some approaches to classification and clustering problems."    

Course workload: 3 credits  

Assessment format: Final exam  

Prerequisites: Basic knowledge of school mathematics, Basic knowledge of statistics, Basic computer skills 

Applied Artificial Intelligence (basic level) (in Russian)

Applied Artificial Intelligence (basic level)

The discipline “Applied Artificial Intelligence (basic level)"" is designed for the students, who have chosen the course “The elements of statistical data processing” in the 1st (autumn) semester.

The discipline consists of two courses. The first course is Introduction to Machine Learning. This course introduces different machine learning types and shows techniques of applied tasks solving by means of machine learning methods. The course mainly focuses on regression types, classification and clustering problems.

The second course is selective, i.e. students can choose between 3 courses: “Artificial intelligence in science and business”, “Automatic text processing” or “Image processing”. It is mandatory to choose one of the courses. 

The course “Artificial intelligence in science and business” shows examples of artificial intelligence methods implementation in different scopes of science, technology and production, such as in information security, industrial automation, speech recognition and synthesis, knowledge graphs, image and text processing. 

The course “Automatic text processing” is devoted to the tasks and problems of natural language processing. After a short review of the history of the field, we consider approaches that work with the language at different levels: from tokenization to syntactic parsing. The main focus of the course is on data-driven machine learning approaches. The course has no prerequisites, except for the previously studied courses in this used learning path. Students can complete the assignment of the course either in Python or using tools.

Among the discussed topics are the following: information retrieval, text classification and evaluation, lexical resources and vector semantics.
The course “Image processing” discusses the main image analysis algorithms. First, several methods of digital image representation and color models are considered. Then, we proceed to image transformations, such as intensity and color transformations, spatial and frequency filtering. The problem of image comparison is thoroughly discussed, both global and local features are considered. Edge and keypoint detection algorithms are described. The assignments of the course are to be made using Python only. 

Course workload: 3 credits 

Course language: English

Prerequisites: The elements of statistical data processing” in the 1st (autumn) semester. 

Assessment format: Final exam   

Applied Artificial Intelligence (advanced level) (in Russian )

Applied Artificial Intelligence (advanced level)

The discipline “Applied Artificial Intelligence (advanced level)"" is designed for the students, who have chosen the course “Introduction to machine learning” in the 1st (autumn) semester.

The discipline consists of two courses. The first course is Advanced Machine Learning. This course discusses the following problems: feature set dimensionality reduction, factor analysis methods, multiclass logistic regression. Students will learn about support vector machines and decision trees, how to build ensembles of models and solve multi-class classification problems. Besides, the course considers the applied problems to be solved by reinforcement learning methods. 

The second course is selective, i.e. students can choose between 3 courses: “Artificial intelligence in science and business”, “Automatic text processing” or “Image processing”. It is mandatory to choose one of the courses. 

The course “Artificial intelligence in science and business” shows examples of artificial intelligence methods implementation in different scopes of science, technology and production, such as in information security, industrial automation, speech recognition and synthesis, knowledge graphs, image and text processing.

The course “Automatic text processing” is devoted to the tasks and problems of natural language processing. After a short review of the history of the field, we consider approaches that work with the language at different levels: from tokenization to syntactic parsing. The main focus of the course is on data-driven machine learning approaches. The course has no prerequisites, except for the previously studied courses in this used learning path. Students can complete the assignment of the course either in Python or using tools.

Among the discussed topics are the following: information retrieval, text classification and evaluation, lexical resources and vector semantics.
The course “Image processing” discusses the main image analysis algorithms. First, several methods of digital image representation and color models are considered. Then, we proceed to image transformations, such as intensity and color transformations, spatial and frequency filtering. The problem of image comparison is thoroughly discussed, both global and local features are considered. Edge and keypoint detection algorithms are described. The assignments of the course are to be made using Python only.

Course workload: 3 credits 

Course language: English

Prerequisites:  Introduction to machine learning in the 1st (autumn) semester. 

Assessment format: Final exam    

The module’s team

Elena Mikhailova

Associate professor, PhD in Physical and Mathematical Sciences, the head of the module

Natalia Grafeeva

Associate professor, PhD in Physical and Mathematical Sciences

Olga Egorova

PhD in Philological Sciences

Anton Boitsev

PhD in Physical and Mathematical Sciences

Dmitry Volchek

PhD in Engineering

Aleksei Romanov

PhD in Engineering