Please look if you would like to take a sneak peek at the 2025 all-in-one teaching materials and the topics we have taught in the course. The topics covering the use of large language models to seek information will be extended (RAG, multilingual and multimodal retrieval, etc.) in the 2025 course run. This interactive syllabus is this course's primary source of information.
2025-03-19: Submissions due for the first project
2025-03-26: Peer reviews due for the first project
This week, there will be a summary of the first part of the course, which is building an inverted index and querying on local and global scales, as well as the basics of the new generation of indexing based on the embeddings.
There are no lectures on Easter Monday. Slides and other materials are for self-study, to be consulted on Monday 19. 5. 2025.
Invited lecture by Florina Piroi, TU Wien
2025-05-07: Submissions due for the second project
2025-05-14: Peer reviews due for the second project
All you wanted to ask about PV211 Introduction to Information Retrieval (topics taught, and self-studied), final exam, but were afraid to ask.
I will be glad if you get encouraged into course topics and decide to get insight into them by solving [mini]projects. Activities in this direction will be rewarded with several 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, LLM assistant for proof of P=NP!, etc.
- (***) Participation in IR competition at Kaggle.com.
- (***)+ Participation in IR research in my group on research agendas (cf. PV212 research seminar on current topics).
- (***)+ Evaluation and updates of Math Information Retrieval system MIaS and comparing it with the new RAG/LLM approaches - possible as a Dean project or 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