Artificial intelligence is a sub-field of computer science that deals with the design and development of computer systems that possess characteristics (ability to solve problems, represent knowledge, infer, learn, etc.) related to the intelligence in human behaviour. Today, the development of methods, technologies, and applications of artificial intelligence is very rapid in the engineering fields and the humanities and social sciences. Conversational bots, product recommendation systems, news bots, virtual assistants, neural network-based medical diagnosis, emotionally intelligent tutoring systems, digital art, and the classification of cultural heritage images in digital libraries are just a few examples of existing solutions. Such a rapidly growing role of artificial intelligence in the modern and future society emphasizes the demand for academically educated professionals who have mastered the fundamentals of artificial intelligence, know its perspectives and have experience in solving the tasks of artificial intelligence to cope with the variety of challenges facing finance professionals, education and medical staff, museum and library staff, etc. This study course focuses on the construction of a problem state space graph and searching for a problem solution using uninformed and heuristically informed search algorithms (search), representation of knowledge about problems with different knowledge representation schemes (knowledge representation), the discovery and generalization of past data models to apply them to new data in tasks such as classification, predicting, finding data similarities and others (machine learning), as well as the basics of natural language processing and computer vision. The methods to be mastered in the study course are demonstrated by looking at examples of their application in the humanities and social sciences. The practical assignment on knowledge representation allows students to train in problem knowledge presentation and apply the tools developed for this purpose in practice. The practical assignment related to the selection, analysis and processing of a dataset ensures the strengthening of knowledge in machine learning. The study course uses a flipped-classroom approach: students independently study the study materials available in the e-study course, devoting lecture time to solving practical tasks working in pairs or small groups. The practical tasks offered in the lectures can be solved both manually and using freely available computer tools for specific tasks.