Lab Seminar

The lab members meet once a week at a lab seminar. Starting from spring 2016 this seminar is officially registered as the IV127 course. The language of the seminar is typically Czech (unless we have some visitor), the instructions are nevertheless in English (to fit with the rest of the lab website and to provide orientation for visitors).

The seminar has two parts: "presentation" and "free discussion". For those enrolled in the IV127 course the first part is compulsory. The seminar is open to anybody interested.


Presentations are of the following types:

  • Tutorial / overview / introduction to a specific topic (typically by senior lab members)
  • Research report (report on a recent paper from closely relevant conference or journal)
  • Progress report (report on students own work - implementation of systems, data analysis)
  • Presenters are expected to have slides and prepare the presentation thoroughly. Presentations are nevertheless interactive (discussion during the presentation). Slides should contain mainly "pictures" (illustrations, graphs) or equations (minimize text, bullet points). Presentations should try to be stand alone (minimal prior knowledge necessary).

    Typically there are two "smaller" presentations (approx. 20 minutes + discussion each) or one "larger" presentation (approx. 40 minutes + discussion).

    Free discussion

    Discussion of hot issues, brief progress reports, results of data analysis, ... This part may depend on "prior knowledge" about the work done in the laboratory, discussion thus may be hard to follow and understand. Although it is often the most interesting part of the seminar, it is thus not compulsory.


    These references are intended mainly for students starting to work within the laboratory. The referenced books are often available in FI library.

    High-level overviews, motivation
    Student modeling, educational data mining

    Overviews of notions and techniques directly relevant to our research.

    Machine learning, statistics

    At least basic knowledge of machine learning and statistics is necessary for most of our research and development.

    • Highly recommended sources
    • Advanced sources (not necessary for most of the work done within our group, but definitely useful)
    Other related areas

    It is useful to have understanding of at least the basic notions in the following domains:

    • Item response theory:
    • Collaborative filtering:
      • Matrix factorization techniques for recommender systems (Koren, Bell, Volinsky)
      • Advances in Collaborative filtering (Koren, Bell)
    • Cognitive psychology:
      • Cognitive psychology (Eisenck, Keane)
    • Education, learning sciences:
      • Applying the Science of Learning (Mayer)