I am responsible for organizing and lecturing several courses at Graz University of Technology. Please find the information of the courses below.
I am responsible for organizing and lecturing several courses at Graz University of Technology. Please find the information of the courses below.
The course introduces the fundamental concepts and underlying algorithms of recommender systems, including content-based filtering, collaborative filtering with a focus on matrix factorization, hybrid recommendation, knowledge-based recommendation, and group recommendation approaches. In addition, the course covers related topics such as explanation techniques, video recommender systems, evaluating recommender systems, and biases in recommender systems. Through this course, students gain both a solid theoretical foundation and an understanding of practical challenges in the design and assessment of recommender systems.
The course presents modern algorithms and methods that reflect the state of the art in configuration knowledge representation, interacting with configurators, as well as intelligent approaches to testing and debugging configuration knowledge bases.
The course provides a foundational introduction to the core concepts and techniques of object-oriented software development. The course covers key topics such as requirements engineering, class diagrams, state charts, object–relational mapping, design patterns, automated testing, and fundamental software design principles. Through continuous hands-on exercises and practical assignments, students deepen their understanding, actively apply the learned concepts, and develop the skills needed to design well-structured and maintainable software systems.
The course introduces a range of software process models and offers guidance on selecting and applying appropriate processes in different development contexts. The course also covers essential aspects of high-quality software requirements, along with fundamental techniques for requirements prioritization and effort estimation.
The course offers an introduction to the theoretical foundations of explainability in AI, with a particular focus on explanation approaches in both logic-based and sub-symbolic AI systems.
In addition to teaching, I also serve as a supervisor and co-supervisor for Bachelor’s and Master’s theses.
Co-supervisor of the Master thesis on “Application of Large Language Models in Usability Testing of Recommender Systems”, 2025. (Contact information: Naida Nozics, naida.nozic@student.tugraz.at)
Co-supervisor of the Master thesis on “Decision Biases in Food Recommendation Systems”, 2023. (Contact information: Amela Kurtic, amelakurtic.97@gmail.com)
Supervisor of the Bachelor thesis on “The Impacts of Primacy/Recency Effects on Item Review Sentiment Analysis”, published at IntRS’22: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, September 22, 2022, Seattle, US (hybrid event). (Contact information: Besnik Gjergjizi, besnik.gjergjizi@student.tugraz.at)
Supervisor of the Bachelor thesis on “The immunity of Users’ Item Selection from Serial Position Effects in Multi-Attribute Item Recommendation Scenarios”, published at IntRS ’21: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (2021), Amsterdam on September 25 or October 1, 2021. (Contact information: Carmen Isabella Baumann, carmen.baumann@student.tugraz.at)