Recommendations for the Lectures

When designing your lecture please keep in mind the most relevant guidelines:

  • The lecture should present the content in a way that allows all participants to develop a true understanding of the most relevant concepts. To achieve this goal you should
    • focus on few, but crucial issues,
    • arrange the information from the literature in a way that is most beneficial for the audience, instead of simply retelling what you have found elsewhere,
    • contain illustrations and examples,
    • complement the technical details by an outline of their relevance for NLP in general, and
    • build bridges to other areas and applications of NLP by highlighting similarities and differences
  • You should only try to explain things, you really have understood well enough. If in doubt, rather formulate a question of what exactly you did not understand. We will then try to find the answer in collaboration among all the particpants.
  • Try to restrict yourself to the topic chosen. Avoid any interference with other topics to be presented by other participants. For example, do not cover Hidden Markov Models if your topic is Markov Chains, or Representation Learning in general if you speak about Word Representation in particular.
  • The literature recommendations given in the list of topics are exactly this: recommendations to get a first quick overview on your topic. As always in science you are expected to do your own search for additional (or better) sources on the web or in your library.

-- WolfgangMenzel - 07 Apr 2022
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