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Аrtificial intelligence technology: summer school
27 June - 8 July 2022
Ivan Franko National University of Lviv
Аrtificial intelligence technology: summer school in Lviv University
- to stimulate the creation of innovative programs on artificial intelligence and update existing ones;
- to promote the involvement of experts in order to form qualifying requirements for educational programs and cooperation with the university in general;
- to involve school participants, teachers, mentors, and industry professionals to work on real projects and research that will occur as a result of schoolwork
Direct target audience:
- 70% of IT students (approximately 420 registrations expected)
- 30% -other students. The participants will be selected based on the topic justification which the applicant is working on or studying at school (approximately 180 registrations are expected)
- 30% of school speakers will be teachers who will test the topics, which eventually will be included in the new educational program
Indirect target audience:
- Teachers involved in teaching at the school
- Authors of the future educational program
- Ivan Franko National University teachers and other teachers from Institutions of Higher Education of Lviv Region and Ukraine,
- Society and the general public of the city, who will find out about the school and the beginning of a new direction at Lviv University.
- 2021, 371 registrations, 165 successfully completed their studies. 14 companies and 23 speakers took part in the work of the school.
Mentors and speakers of the school will be researchers, developers, and skilled professionals with proven experience on practical/real projects in specific area
Working languages of the school: English and Ukrainian. Language of working materials: English
Oksana Pomorova, Senior ML/DL Developer, GlobalLogic
PhD, computer scientist, over 20 years of experience in the field of artificial intelligence. Scientific interests are focused on the development of intelligent software for Computer Vision, Natural Language Processing, Recommender Systems and Ontologies.
We will consider Multi-task learning. This is a machine learning approach in which we try to learn multiple tasks simultaneously, optimizing multiple loss functions at once. Rather than training independent models for each task, we allow a single model to learn to complete all of the tasks at once. In this process, the model uses all of the available data across the different tasks to learn generalized representations of the data that are useful in multiple contexts.
Multi-task learning has seen widespread usage across multiple domains such as natural language processing, computer vision, and recommendation systems. It is also commonly leveraged in industry, such as at Google, due to its ability to effectively leverage large amounts of data in order to solve related tasks.
With participation of
How to join?
1) To provide the school with a speaker or mentor;
2) to support the project implementation by becoming its partner.
Advantages of partnership:
- additional promotion of your company among students;
- communication and involvement of the students into the company's activities;
- distribution of information content and posts about the company in the social networks of the University;
If you want to take part in schoolwork or cooperate with us, send us an email: marketing@ lnu.edu.ua