Modern methods of artificial intelligence

Features of the master's program

Courses are taught in English by highly qualified teachers and experts in the field, including Andrey Raigorodsky, Radoslav Neychev, Alexander Dainyak, Vladislav Goncharenko, Anastasia Yanina, Maxim Zhukovsky and Yury Efimov. Partners are leading companies in the field of artificial intelligence: Yandex, Sberbank and others.

The program focuses on: Is computer science engineering

practical experience of students to work on AI applications

real cases from companies and examples from scientific projects

interactive educational process

Graduates will receive:

comprehensive knowledge of machine and deep learning, as well as the ability to correctly apply these methods

Competencies in distributed and cloud computing

the ability to draw useful conclusions and present them in an informative way

the skill of creating stable and productive software, as well as reliable and resilient data lines

the ability to formulate real problems in technical language and find optimal approaches to solve them

general understanding of the current state and development trends of the AI ​​sphere

The educational program includes:

e-courses, the pace of learning in which varies by the student

weekly webinars and consultations with teachers

individual supervision of scientific work

intermediate certification using proctoring

Main courses

Introduction to artificial intelligence

The course introduces students to the current state of machine learning and artificial intelligence: from classical algorithms to deep learning approaches and the latest advances in artificial intelligence. As a result, students form a solid foundation for further growth in AI.

Computer vision

Methods for efficiently processing and extracting knowledge from visual information are needed for applications such as computational photography, self-driving cars and aerial vehicles. Despite the rapid progress of the last decade, this area is still full of opportunities for development. So now is a great time to dive into it, armed with classic image processing techniques combined with deep learning.

Reinforcement learning

Reinforcement learning is a fairly young but very promising direction in the field of artificial intelligence. New achievements and discoveries regularly appear in it. Artificial agents started out with tic-tac-toe, but today they surpass humans in chess, computer strategy like StarCraft and real-life 3D Rubik's cube. The game is not limited to games: Reinforcement learning methods are used in computer vision, expert systems, natural language processing, including machine translation, and so on.

Natural language processing

Natural Language Comprehension (NLP) is one of the keys to organic human-machine interaction. New developments in this area are leading to tangible improvements in search engines, chat bots, machine translation, and AI in general. NLP is one of the most profitable destinations, and there is always room for improvement and learning.

Software development and cloud computing

To create a successful AI system, you need quality models, efficient and well-written code, as well as professional knowledge of the hardware and the ability to work in a team. All this takes practice. Implementation of models, their deployment on various systems, including embedded ones (on smartphones), construction of data transmission lines are the steps that are necessary to create high-quality software. Students will go through them one by one.