Curriculum

This program is a new and applied online master’s degree in Computer Vision that is designed by leading experts at companies such as Huawei, Itseez3D, Intel, Harman, and Experience AI, as well as HSE scholars involved in advanced research within the computer vision field with publications in top-ranked journals.

You’ll be able to build on your undergraduate bachelor's degree in IT, engineering, technology, or mathematics and immerse yourself in the modern, dynamically developing, and universally demanded field of computer vision, creating a solid foundation for your professional career.

In this program, you’ll have access to the following benefits and competencies:

  • Experience in training with highly qualified specialists in the field of computer science.
  • A modern curriculum created in collaboration with leading experts in the field of computer vision.
  • Large bases of projects informed by partner companies. The program consists of 16 online training courses on the Coursera platform, divided into 4 blocks "mathematics", "programming", "professional block", "project block" and a large final project:
  • Basic courses in the field of mathematics and IT, necessary for the further development of disciplines.
  • A professional block containing a modern theoretical and instrumental base in the field of computer vision. This block is focused on the study of theoretical approaches and tools for solving applied problems encountered in professional activities.• a project block that allows to test all the knowledge gained within the professional block on applied projects provided by partner companies.
  • A final project providing you the opportunity to demonstrate a complexity of knowledge (both theoretical and practical) acquired in the program.

Courses

Object-oriented programming

The ability to write programs using object-oriented programming languages is an important skill for engineers working with computer vision. The course will cover two object-oriented languages: Python and C ++. These object-oriented languages are most often used when working on projects in the field of computer vision.

Mathematics for computer vision

This course is devoted to the systematization of the mathematical background that is necessary for you to master prior to entering the field of computer vision. The course includes sections of mathematical analysis, probability theory, and linear algebra.

2D image processing

This course is devoted to the usage of computer vision libraries like OpenCV in 2D image processing. The course includes sections of image filtering and thresholding, edge/corner/interest point detection, local and global descriptors, and video tracking.

Modern operation research methods

The discipline is designed to develop skills in working with information about open and recently-solved problems from various fields of operations research and computer science, as well as develop approaches to these problems.

Data analysis and machine learning

This course is devoted to the presentation of modern data analysis and machine learning methods that are widely used computer vision. The main emphasis is placed on such sections as:

  • Learning and inference in vision, where you’ll study a taxonomy of models that relate the measured image data to the actual scene content.
  • Generative and discriminative models Classification, regression, and clustering methods.

Machine learning in computer vision project

The project will be considered in the first semester after you study the basic adaptation disciplines of mathematics and programming blocks, as well as studying the professional course “Data analysis and machine learning”. The goal of this project is to solve an applied problem for the analysis of two-dimensional images using machine learning technology.

Deep learning in computer vision

The goal of this course is to introduce you to deep learning algorithms for computer vision, starting from basics and then turning to more modern deep learning models. The course will cover image classification, object detection, and semantic segmentation algorithms. You'll learn how to build accurate algorithms for a given task using state-of-the-art algorithms and tools.

Architecture of computing systems

This course focuses on the architecture of modern computers. We’ll cover the evolution of computer architecture and the factors influencing the design of hardware and software elements of computer systems. We’ll also discuss the processor micro-architecture and pipelining, memory organization, and various types of the modern hardware architecture.

Visual geometry and 3D image processing

The course is focused on geometry models used for understanding of 3D scenes. The main emphasis is placed on such sections as: projective pinhole camera model, 2D/3D image transformation models, multiple cameras, and multi-view reconstruction.

Applied tasks of computer vision research seminar

Computer vision technologies are used in various fields of human activity. They are medicine, automotive technologies, the creation of video surveillance systems, the entertainment industry, etc. The problems that arise attract the attention of a large number of researchers and practitioners. This research seminar is focused on analysis of new research papers on the topic of computer vision.

Modern tools for solving computer vision problems

Many sophisticated Computer Vision and Deep Learning tools are emerging nowadays. You'll become familiarized with state-of-the-art libraries and tools for:

  • model training;
  • efficient inference, including inference on edge devices;
  • and working with 2Dimages, videos, and 3D data.

“Deep learning in computer vision” coursework

In this coursework you'll analyze the benefits of deep learning approaches over traditional machine learning technologies and complete an applied project that demonstrates the identified benefits.

Deep generative models

The course covers the key techniques that have dominated the generative modeling of images. The course includes sections of variational autoencoders, generative adversarial networks, and style transfer.

Software engineering of computer vision projects

The success of a computer vision project depends not only on the algorithms and models, but also on the solution architecture, performance, and robustness. Thus software engineering is an important skill for engineers working with computer vision. The course will cover best engineering practices for large-scale projects that heavily rely on deep learning and computer vision.

“Computer vision of mobile devices” project seminar

The project seminar is devoted to the usage of either traditional image processing or deep learning models in Android mobile devices. The course includes sections of modern tools for mobile programming, implementation of computer vision algorithms in mobile applications, and transformation and optimization of deep learning models for mobile devices. The submission of the material is organized in a step-by-step format from the analysis of the idea to the implementation of the application on a mobile device.

Program Length

The program is designed so students can enroll from anywhere in the world and complete courses at their own pace. As a designated two-year program, it is recommended that students take between 20 to 24 months to complete the program.

Flexibility

The program is 100% online. The online format of the degree also allows students to interact with instructors and teaching assistants regularly through live chats and video conferences.

Coursera on Mobile

Access all course materials anywhere with the mobile app, used by over 80 percent of degree students on Coursera. Available on iOS and Android.

Using the mobile app, learners can:

  • Save a week’s worth of content for offline access with one click
  • Save and submit quizzes offline
  • View text transcripts of lecture videos
  • Take notes directly in the app
  • Set reminder alerts to help you make progress

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Coursera does not grant credit, and does not represent that any institution other than the degree granting institution will recognize the credit or credential awarded by the institution; the decision to grant, accept, or transfer credit is subject to the sole and absolute discretion of an educational institution.

We encourage you to investigate whether this degree meets your academic and/or professional needs before applying.