Basics of Artificial Intelligence and Logic: Introduction to basic symbolic AI techniques such as search, planning, constraint satisfaction, configuration, and diagnosis. In this context, relationships to subsymbolic AI approaches are discussed where relevant.

Recommender Systems: Recommender systems are well known from their application in online sales platforms such as Amazon.com. A major focus of this course is to introduce basic recommendation algorithms such as content-based filtering, collaborative filtering (especially matrix factorization), hybrid recommendation, knowledge-based recommendation, and group recommender systems. As an emerging core technology, LLM-based recommendation is also included as a subtopic.

Configuration Systems: Example application areas of configuration systems include the automotive domain, financial services, elevators, and software components. In this context, algorithms and techniques are introduced that provide insights into the state of the art in configuration knowledge representation, interaction with configurators, and intelligent testing and debugging of configuration knowledge bases.

Object-Oriented Analysis and Design (OAD): A basic introduction to the concepts of OAD, specifically Requirements Engineering, Class Diagrams, State Charts, Object-Relational Mapping, Design Patterns, Automated Testing, and Software Design Principles. LLMs are included as a supportive technology where appropriate, for example, in the context of requirements engineering.

Introduction to Structured Programming: Basic introduction to programming techniques on the basis of a selected programming language.

Software Development Process: Introduction to different types of software processes, including corresponding insights into when and how to apply a particular process. Overview of relevant aspects of high-quality software requirements. Introduction to basic techniques of prioritization and effort estimation. LLMs are included as a supportive technology where appropriate, for example, in the context of requirements quality assurance.

Explanations in AI (XAI): Basic introduction to the foundations of explanations in Artificial Intelligence, with a specific focus on explanations in both, logic-based and subsymbolic AI contexts. LLMs are included as a supportive technology where appropriate, for example, in the generation, refinement, and evaluation of explanations. The explanations course is also offered in adapted form to students of the University of Oulu.