Artificial Intelligence Technologies and Big Data
Source: https://english.spbu.ru/admission/programms/graduate/artificial-intelligence-technologies-and-big-data Parent: https://english.spbu.ru/admission/programms/graduate
02.04.02 In Russian
Level of education Master
Type of instruction Full-time
Duration 2 years
Programme description
- The programme prepares specialists for independent research and development work in artificial intelligence and big data fields. Graduates will develop expertise in data collection, intelligent analysis, structuring and formalisation of diverse data types, along with data storage organisation and security, as well as solving applied problems using cutting-edge AI technologies
- The 'Artificial Intelligence Technologies and Big Data' programme is designed to provide educational support for highly skilled AI and big data professionals by equipping them with knowledge about contemporary data mining methods, algorithms, and implementing software tools. The programme is designed within federal 'Artificial Intelligence' initiative of the Russia's 'Digital Economy' National Programme
Main courses
- Introduction to Artificial Intelligence
- Machine Learning
- Modern Software Development Methodologies
- Contemporary Database Management Systems
- Statistical Data Processing Methods for AI Applications
- Python for Data Analysis
- Software Engineering
- Automated Data Collection and Processing Systems
- Deep Learning
- Big Data: Theory and Practice
- Natural Language Processing
- Computer Vision
- Speech Processing
- Reinforcement Learning
- IT Business Analysis
Our advantages
- The master programme 'Artificial Intelligence Technologies and Big Data' was developed with the support of major industry partners specialising in big data, artificial intelligence, and other IT fields
- The programme offers an individual learning pathway, allowing students to tailor their studies to develop specialised professional competencies in applying specific artificial intelligence technologies
- Our teaching faculty comprises distinguished academic researchers in artificial intelligence alongside seasoned professionals from leading industry organisations specialising in AI and big data technologies
- The programme offers a project-oriented internship with a unique methodology with support from our industry partners specialising in big data and artificial intelligence
- The programme facilitates networking and career opportunities within its partner organisations specialising in big data systems development and artificial intelligence
Teaching staff
- I. Blekanov – Candidate of Technical Sciences, Associate Professor. His research expertise spans data science, web science and webometrics, natural language processing (NLP), computer vision, image processing, information retrieval, time series analysis and forecasting, neural networks, social network analysis (SNA), and digital forensics. Professor Blekanov is a recipient of the St Petersburg Government Award for achievements in research and teaching. Along with five state-registration certificates for software in artificial intelligence and big data collection he has authored over 60 research publications indexed in RSCI, Scopus and Web of Science databases,
- A. Krylatov – Doctor of Physics and Mathematics, specialises in network flow distribution, network topology optimisation, bilevel optimisation, and mathematical modelling of socio-economic systems. With over 100 academic publications, including works indexed in RSCI, Scopus and Web of Science, he serves as a member of the Governing Board of the world-class research and education centre Engineering of the Future. At SPbU, he coordinates the working group on implementing Russia's 'Digital Economy' programme in educational programmes
- O. Granichin – Doctor of Physics and Mathematics, Professor. He specialises in multi-agent adaptive control, intelligent embedded systems, data clustering (data mining), theoretical computing, adaptive and optimal control systems, randomised algorithms for multidimensional estimation and optimisation, system identification, learning systems, and quantum computers, among other fields. His extensive publication record comprises over 300 research works indexed in RSCI, Scopus, and Web of Science
- V. Bure – Doctor of Technical Sciences, Professor. He conducts research in probabilistic-statistical modelling and data analysis. He has authored over 200 two academic publications indexed in RSCI, Scopus, and Web of Science
Main areas of research
- Formulation and proof of new theoretical results in artificial intelligence and big data
- Development of new or modification of existing machine learning methods and neural network architectures, with evaluation of their performance and domains of effective application
- Development of new or refinement of existing big data analysis methods, demonstrating their efficacy through relevant real-world data applications
- Development of software solutions in artificial intelligence and big data fields
- Design of neural network methodologies employing transfer learning for user-generated content analysis at diverse social media platforms
- Development of neural network-based approaches for textual sentiment analysis in social network communications
- Analytical methods for cardiac medical imaging of aorta (conducted in collaboration with the N. Pirogov Clinic of High Medical Technologies)
- Development of satellite-based sensing methodologies for detecting water stress and soil nitrogen levels in agricultural fields (conducted in collaboration with the Agrophysical Research Institute of the Russian Academy of Sciences)
- Neural language model applications for code autocompletion systems
- Deep learning approaches for tumour detection and segmentation in medical imaging
- Intelligent analysis and generation methods for digital audio signals
Career opportunities
Internships at
- Digital Assembly
- Special Technology Centre
- Gazprom Neft
- Huawei Tech Company
Professions
- Research and Development Specialist (R&D Specialist)
- IT Project Manager
- Programmer
- Data Scientist
- Machine Learning Engineer
- Data Engineer
- Data Analyst
- Software Architect
- Big Data Specialist
- Head of Software Development