👷 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 2023 all-in-one 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 assignment
CQADupStack Collection and the ARQMath Collection
Second term project assignment (CQADupStack Collection)
Google Colaboratory code for the second term project
Second term project leaderboard (CQADupStack Collection)
Google Spreadsheet leaderboard for the second term project
Alternative second term project assignment (ARQMath Collection)
Google Colaboratory code for the alternative second term project
Alternative second term project leaderboard (ARQMath Collection)
Google Spreadsheet leaderboard for the alternative second term project
Second term project Jupyter Hub
Dedicated computational resource for your second term projects
Second term project (CQADupStack Collection)
Homework vault for the second term project (a ranked supervised retrieval system for the CQADupStack Collection).
Alternative second term project (ARQMath Collection)
Homework vault for the alternative second term project (a ranked supervised retrieval system for the ARQMath Collection).

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Web
Učitel doporučuje studovat od 13. 2. 2023 do 19. 2. 2023.
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Učitel doporučuje studovat od 20. 2. 2023 do 26. 2. 2023.
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Učitel doporučuje studovat od 27. 2. 2023 do 5. 3. 2023.
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Učitel doporučuje studovat od 6. 3. 2023 do 12. 3. 2023.

2023-03-13: Submissions due for the first-term project

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Učitel doporučuje studovat od 13. 3. 2023 do 19. 3. 2023.

2023-03-20: Peer reviews due for the first-term project

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Učitel doporučuje studovat od 20. 3. 2023 do 26. 3. 2023.
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Učitel doporučuje studovat od 27. 3. 2023 do 2. 4. 2023.
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Učitel doporučuje studovat od 3. 4. 2023 do 9. 4. 2023.
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Učitel doporučuje studovat od 10. 4. 2023 do 16. 4. 2023.
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Učitel doporučuje studovat od 17. 4. 2023 do 23. 4. 2023.
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Učitel doporučuje studovat od 20. 4. 2023 do 30. 4. 2023.

2023-05-01: Submissions due for the second-term project

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Učitel doporučuje studovat od 1. 5. 2023 do 7. 5. 2023.

2023-05-08: Peer reviews due for the second-term project

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Učitel doporučuje studovat od 8. 5. 2023 do 14. 5. 2023.

The following topics will not be covered in the 2023 course run:

Here are materials from the previous runs of the course: spring 2019, spring 2020, spring 2021, and spring 2022

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