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.
2022-03-14: Submissions due for the first term project
2022-03-21: Peer reviews due for the first term project
2022-05-02: Submissions due for the second term project
2022-05-09: Peer reviews due for the second term project
The following topics will not be covered in the 2022 course run:
- XML retrieval
- the European digital mathematical library: an overview of math-specific technologies
- probabilistic information retrieval
- language models for information retrieval
- crawling
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 Michal Štefánik 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