The course is designed to teach students the technology of creating intelligent systems based on the concept of co-evolutionary hybrid intelligence. It describes the basic concepts of co-evolutionary hybrid intelligence (CHI) and cognitive architecture for its implementation.. The detailed discussion for each block of the architecture is provided including methods and implementation details. In the last part of the course the applications of co-evolutionary hybrid intelligence are presented. The course includes practical exercises to reinforce the theoretical material. In order to obtain the skills of designing intelligent systems based on the concept of CHI, students are doing capstone projects in teams. The course is structured in such a way as to give students a comprehensive understanding of the process and approaches to the design of a new generation of intelligent systems.

This mini-course aims to provide a solid foundation in DevOps and MLOps for intelligent human-in-the-loop systems, focusing on the essential concepts, tools, and practices. The course focuses on the skills required to set up basic pipelines using these tools. An explanation of modern most advanced concepts gives a deeper understanding of specialized areas like "Infrastructure as Code" (IaC), Configuration Management, microservices architecture, and monitoring and alerting systems in DevOps. For MLOps, it elaborates on managing ML model lifecycle, model versioning, model serving, and similar monitoring and alerting systems. Through hands-on training and capstone projects participants will apply the learned concepts practically, This will contribute to developing a deeper understanding of the implementation of intelligent human-in-the-loop systems and gaining confidence to apply these technologies in their daily work

The course offers an in-depth exploration of AI principles, focusing on narrow AI, neural networks, machine learning, and evolutionary modeling. It also delves into knowledge management and representation, featuring different models and systems. Practical exercises form a core part of the curriculum, including tasks such as data manipulation using SQL and UML, neural network creation and training, implementation of various classifiers and clustering algorithms, regression analysis for finding dependencies, and optimization of neural networks with genetic algorithms. These hands-on tasks, using diverse datasets, aim to solidify theoretical understanding with practical application, providing a well-rounded introduction to AI.