Recommender Systems: the Fundamentals

Recommender Systems: the Fundamentals

Course Description

This course is designed for university students of all levels and researchers interested in Recommender Systems. This course will guide you through the following topics: 1) Presentation of Recommender Systems as a real-life application of Data Mining and Data Science; 2) Overview of methodological solutions and different approaches that can be used to build such systems; 3) Discussion of possible aspects of practical implementation.

Course tools

  • Python
  • Jupyter Notebook


  • Python programming: intermediate level
  •  Linear algebra and matrices: university level
Level of complexity of course Intermediate


Dr. Marharyta Aleksandrova The University of Luxembourg. After completing a Master’s degree from the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Marharyta started her research path by pursuing a joint doctorate program between France (University of Lorraine) and Ukraine (Igor Sikorsky KPI). Currently, she holds the position of postdoctoral researcher at the University of Luxembourg. During these years, she has been exposed to various applications of Data Science and worked on several European and national research projects.. Fields of interests: Data analysis, Recommender systems, Application of Data Mining to security, Theory of machine learning (causal learning, conformal learning) Contacts:

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