Learning from User-generated Data

This class is usually taught by Markus Schedl in the summer term. The class is taught in English.

Information for the current semester (if available):

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Class Objectives and Content

Students will acquire basic knowledge for dealing with used-generated data in the fields of machine learning and pattern recognition. They will learn about sources and methods for data extraction from the web and social media as well as techniques for processing this data. The students' skills will be further developed through a practical project involving work with real-world data.

The main topics covered include: 

  • sources for user-generated data
  • mining and analysis of web content and structure
  • mining and analysis of social media
  • mining of user behaviour and feed-back (explicit and implicit)
  • recommender systems
  • strategies for personalisation of content and user interfaces
  • context-aware search, retrieval, and recommendation

 

Practical Exercise

Using real-world, user-generated data, students will conduct several tasks centered around the topic of recommender systems.