Training “Savarankiškai besimokantis dirbtinis intelektas” (Self-Learning AI)

On 13 November at 18:00, the Artificial Intelligence Competence Center SustAInLivWork invites you to an EU-funded training, which in just 1.5 hours will help you better understand how artificial intelligence learns without pre-provided examples.

Please note: The training will be conducted in Lithuanian.

Artificial Intelligence (AI) is increasingly becoming part of our daily lives – recommending music, assisting doctors in decision-making, or detecting cases of fraud. In this training, we will explore how AI can learn independently, without human instructions, by identifying patterns and hidden structures within data. Participants will be introduced to unsupervised learning algorithms and their applications in various fields: from science and technology to everyday life.

The training will help you understand how this type of learning enables AI to assist humans in discovering new knowledge, and why it is essential for people to remain not just observers but critical evaluators of AI-driven insights.

The training will be led by Dr Kristina Šutienė, an expert at the AI Competence Center SustAInLivWork. With over 15 years of experience in AI, data science, and mathematical modeling, she is an active researcher developing advanced AI methods and their applications.

Her professional work includes developing and applying data analysis methods, designing advanced numerical models, and integrating them into practical solutions. She is also actively involved in applied and scientific projects where AI solutions are used to address challenges across various domains: from process optimization to decision-support system development.

Assoc. Prof. Kristina Šutienė
Assoc. Prof. Kristina Šutienė

The training will take place online via the TEAMS platform. Registered participants will receive a joining link by email one day before the event.

Co-funded by the European Union logo
The project is co-funded under the European Union’s Horizon Europe programme under Grant Agreement No. 101059903 and under the European Union Funds’ Investments 2021–2027 (project No. 10-042-P-0001).