The course moved from its web presentation to IS MU in 2011. Please look if you would like to take a sneak peek at the 2024 all-in-one teaching materials and the topics we will discuss in the course. However, this interactive syllabus is this course's primary source of information.
2024-03-19: Submissions due for the first project
2024-03-26: 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.
Question Answering, Extractive Question Answering, Abstractive Question Answering, Maximum Marginal Likelihood, LLMs vs QA
2024-05-12: Submissions due for the second project
2024-05-19: Peer reviews due for the second project
Here are materials from the previous runs of the course: spring 2019, spring 2020, spring 2021, spring 2022 and spring 2023
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, 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 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