This course should provide students with an overview of available options in the domain of expert and knowledge-based systems, as well as show them some possible research directions in the field. Students should acquire understanding of proinciples upon which knowledge engineering is based, such as: rules, semantic networks, knowledge acquisition and fuzzy logic.

During the course, students will be introduced to knowledge-based systems, expert systems, intelligent agents and decision support systems. They will learn how to use data mining methods and techniques and how to combine them with knowledge management tools in order to create a knowledge base within the domain in the focus of interest. They will also learn how to use aforementioned methods, techniques and tools to design a recommendation system for that knowledge base.

Introduction to knowledge management. Knowledge management systems. SECI model. IT and knowledge management. Databases and data warehouses. Introduction to data mining. Data retrieval and transformation. Data mining techniques and methods. Expert systems. Business intelligence. Collaborative platforms. Challenges and trends in the domain.

The course comprises classes and computer lab exercises. Knowledge assessment is done through individual project and oral exam.