👷 Introduction to Information Retrieval
doc. RNDr. Petr Sojka, Ph.D.
👷 Introduction to Information Retrieval

Dear students,

welcome to the PV211 Introduction to Information Retrieval course.

The course is based on the Introduction to Information Retrieval textbook by Manning, Raghavan and Schutze (hard copies available in MU libraries) taught at Stanford, Munich, and other places. In the course you will, among other things, learn how it is possible to fulfill seekers' information needs at the pace of 10,000+ questions per second on the global web scale within milliseconds.

Students will be motivated to try active/flipped learning approaches wherever possible.

The course has moved from its own web presentation to IS MU. Please, have a look if you would like to take a sneak peek at the all-in-one preliminary teaching materials and the topics that we will discuss in the course. However, this interactive syllabus is the primary source of information in this course.

Course trailer (in Czech)
A trailer for the PV211 Introduction to Information Retrieval course by Tomáš Effenberger
Second term project assignments
Slides introducing the second term project by Vít Novotný
Second term project assignments (GitHub)
Google Colaboratory code for the second term project
Second term project assignments (JupyterHub)
A JupyterHub cluster kindly provided to the course by ICT MU. You can use JupyterHub to work on your first term project assignment. Compared to Google Colaboratory, JupyterHub offers up to 32 CPUs, 2 NVIDIA A40 GPUs and 64G RAM. Notebooks will be closed after 3 days of inactivity; make sure you download your work!
Second term project leaderboard (TREC collection)
Google Spreadsheet leaderboard for the second term project
Alternative second term project leaderboard (ARQMath collection)
Google Spreadsheet leaderboard for the alternative second term project

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Teacher recommends to study from 9/2/2022 to 20/2/2022.
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Teacher recommends to study from 19/2/2022 to 27/2/2022.
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Teacher recommends to study from 26/2/2022 to 6/3/2022.
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Teacher recommends to study from 5/3/2022 to 13/3/2022.

2022-03-14: Submissions due for the first term project

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Teacher recommends to study from 12/3/2022 to 20/3/2022.

2022-03-21: Peer reviews due for the first term project

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Teacher recommends to study from 19/3/2022 to 27/3/2022.
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Teacher recommends to study from 26/3/2022 to 3/4/2022.
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Teacher recommends to study from 2/4/2022 to 10/4/2022.
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Teacher recommends to study from 9/4/2022 to 17/4/2022.

2022-05-02: Submissions due for the second term project

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Teacher recommends to study from 16/4/2022 to 24/4/2022.
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Teacher recommends to study from 23/4/2022 to 1/5/2022.

2022-05-09: Peer reviews due for the second term project

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Teacher recommends to study from 30/4/2022 to 8/5/2022.
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Teacher recommends to study from 7/5/2022 to 15/5/2022.

The following topics will not be covered in the 2022 course run:

Here are materials from the previous runs of the course:

I will be glad if you get encouraged into course topics and you decide to get insight into it by solving [mini]projects. Activities in this direction will be rewarded by the nontrivial number of premium points towards successful grading. The number of stars below is an estimate of project difficulty, from miniproject [(*), 10 points] to big project size [(*****), 30+ points]. I am also open to assigning/extending a project as a Bachelor/ Masters/ Dissertation thesis. 

  • (*)+ Pointing to any (factual, typographical) errors in the course materials.
  • (**)+ Preparation of Deepnote instructions, documentation, and support for the solution of course projects
  • (**)+ Preparation of hot topic slides, production or preparation of motivating Khan-Academy style video, or other course materials in LaTeX.
  • (**)+ Presentation or teaching video on topics relevant to the course. Possible topics: Sketch Engine, search with linguistic attributes, random walks in texts, topic search and corpora, time-constrained search, topic modeling with gensim, LDA, Wolfram Alpha, specifics of search of structured data (chemical and mathematical formulae, linguistic trees - syntactic or dependency), etc.
  • (***) Participation in IR competition at Kaggle.com.
  • (***)+ Participation in IR research in our group Math Information Retrieval on research agendas and ARQMath task or EuDML project or DML project.
  • (***)+ Evaluation of Math Information Retrieval in system MIaS - possible as a Dean project under the supervision of Vít Novotný or Martin Geletka or  or as a Bachelor/Masters/Dissertation thesis.

To a pupil who was in danger, Master said, “Those who do not make mistakes, they are most mistaken for all – they do not try anything new.” Anthony de Mello

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