Ordered Sets in Data Analysis (2022)
Содержание
General information
One semester course. 6 kredits.
Lecturer: Sergey Kuznetsov (Сергей Олегович Кузнецов)
Class teacher: Fedor Strok (Федор Владимирович Строк)
Schedule
| Type | Day of week | Time | Place |
|---|---|---|---|
| Lectures | Tuesday | 11:10-12:30, 13.00-14.20 | S224, Pokrovsky Blvd. 11 (Покровский б-р 11) |
| Seminars | Tuesday | 11:10-12:30, 13.00-14.20 | S224, Pokrovsky Blvd. 11 (Покровский б-р 11) |
Grading system
Final Grade = 0.45 * first_module + 0.55 * second_module
first_module = 0.5 * avg (homeworks) + 0.5 * test (at the end of the module)
second_module = 0.3 * avg (homeworks) + 0.4 * big homework + 0.3 * test (at the end of the module)
Homeworks -- assignments given after each lecture;
Big Homework -- three options:
- SURVEY:
Choosing this option means that you make a 30-minutes oral presentation with LaTeX-based pdf slides which is a survey of 5+ recent papers (preferably published not earlier than 2018) on the topic (see the list below) preferably taken from Q1-Q2 journals (according to Web of Science or Scopus, www.scimagojr.com) and A-A* conferences (according to CORE conference ranking http://portal.core.edu.au/conf-ranks/) like IJCAI, ICDM, NeurIPS, ICML, ECML/PKDD etc. If you want to choose articles somewhere else, you need to consult with the teacher to estimate the level of the publication you have chosen.
- Lazy FCA
- Neural FCA
Tasks
| Task description | Source | Deadline |
|---|---|---|
| Solve the problems given at the end of the first lecture. The exact problems to solve are: 3-6 | Lecture 1 | 20.09.22 |
Lectures
| Lecture | Date | Topics | Download materials | Russian Materials | Read Materials | Pages | Reading time |
|---|---|---|---|---|---|---|---|
| Lecture 0 | Prerequisites | Asymptotic notations. Complexity classes. P- and NP-complete problems. NDMT. | Click | Missing | Click | 38p | 20-30 min read |
| Lecture 1 | 13.09.22 | Relations, binary relations, their matrices and graphs. Operations over relations, their properties, and types of relations. | Click | Click | Click | 37p | 20-30 min read |
| Lecture 2 | 27.09.22 | Quasi order, partial order. Topological sorting. Dushnik-Miller theorem. Applications. | Click | Click | Click | 54p | 30-40 min read |
| Lecture 3 | 11.10.22 | Lattices and closures. Semilattices. Distributivity and modularity. | Click | Missing | Click | 44p | 30-40 min read |
| Lecture 4 | 11.10.22 | Introduction to Formal Concept Analysis. Concept lattice and implications. | Click | Missing | Click | 35p | 24-26 min read |
Seminars
| Seminar | Date | Topics | Download materials | Read Materials | Pages | Reading time |
|---|---|---|---|---|---|---|
| soon | soon | soon | soon | soon | soon | soon |
References
Main literature
- Cormen, T. H., Leiserson, C. E., Rivest, R. L., Stein, C. Introduction to Algorithms (3rd edition). – MIT Press, 2009. – 1292 pp.
- Kuznetsov, S. O. Fitting pattern structures to knowledge discovery in big data // International conference on formal concept analysis. – Springer, Berlin, Heidelberg, 2013. – PP. 254-266.
Additional literature
- Kuznetsov, S. O. Pattern structures for analyzing complex data // International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing. – Springer, Berlin, Heidelberg, 2009. – P. 33-44.
- Kuznetsov, S. O. Scalable knowledge discovery in complex data with pattern structures // International Conference on Pattern Recognition and Machine Intelligence. – Springer, Berlin, Heidelberg, 2013. – P. 30-39.
