Course information

The course is divided into two parts, focusing on Artificial Intelligence (AI) and Knowledge Bases and Expert Systems. Part A covers foundational AI concepts, including supervised, unsupervised, and semi-supervised learning, probabilistic methods, and intelligent agents. It delves into neural networks, deep learning, classification methods, and state modeling techniques, such as heuristic methods, game theory, and Bayesian classifiers. Part B introduces Knowledge Bases and Expert Systems, exploring symbolic knowledge representation, inference and decision control processes, fuzzy logic, and the development of expert systems. Students will learn advanced mathematical tools for modeling knowledge-based systems and training machine learning models. The course emphasizes practical applications in real-world engineering problems, especially in geoinformatics, and includes hands-on experience with Python and other programming environments. Upon completion, students will be equipped to execute research projects in AI and expert systems for engineering applications.