188.980 Advanced Information Retrieval
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020S, VU, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise

Learning outcomes

After successful completion of the course, students are able to implement basic and advanced concepts of Information Retrieval. More specifically, the students should:

  • Gain a fundamental understanding on how (web) search engines (like Google, Bing, Lucene, Elasticsearch, …) work
  • Learn how to efficiently search a large number of documents and rank them according to their relevance with respect to a given query
  • Learn how to evaluate search results and incorporate additional context information (like PageRank) to improve search results
  • Learn about Deep Neural Networks and how they can be utilized to improve the search effectiveness (e.g. learn to rank)---in that sense, there will be also a short introduction to Machine Learning and the basics of Neural Networks
  • Learn how Neural Networks can be used to create advanced text representations, i.e. Word Embeddings

Information Retrieval is the science behind search technology. Certainly, the most visible instances are the large Web Search engines, the likes of Google and Bing, but information retrieval appears everywhere we have to deal with unstructured data (e.g. free text). The focus of this lecture will be on text IR and music IR.

Differences to the Grundlagen des IR Course (188.977)

  • The basic concepts of IR (inverted index, text pre-processing, etc.) are taught in detail in the Grundlagen course. These concepts, will be only briefly refreshed in the advanced course.
  • One substantial part of the advanced course will be the topics Machine Learning, Deep Learning and Word Embeddings---whereas, in the Grundlagen course, these topics are not covered.


We start with our first lecture on the 3.3.! Awesome! đź‘Ź

Subject of course

Lectures (20 h)

  • Vorbesprechung
  • 2x Crash course IR, 2x Machine learning & data annotation, 4x NLP & Neural ranking

Exercises (40 h)

  • Exercise1 (Data annotation): 10 h
  • Exercise2 (Neural re-ranking in Pytorch): 30 h

Exam (15 h)

  • Preparation: 14 h
  • Exam: 1 h

Total (75 h)

Teaching methods

Programming Neural Networks in PyTorch

Mode of examination




Course dates

Tue17:00 - 19:0003.03.2020 - 10.03.2020FAV Hörsaal 1 - INF Lecture
Advanced Information Retrieval - Single appointments
Tue03.03.202017:00 - 19:00FAV Hörsaal 1 - INF Lecture
Tue10.03.202017:00 - 19:00FAV Hörsaal 1 - INF Lecture

Examination modalities

Exercise and Exam

Course registration

Begin End Deregistration end
01.02.2020 00:00 30.03.2020 23:59 30.03.2020 23:59



No lecture notes are available.

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