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добавил лекции по осда

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== General information ==
One semester course. 6 kredits.

Lecturer: [https://www.hse.ru/staff/skuznetsov Sergey Kuznetsov] (Сергей Олегович Кузнецов)

Class teacher: Fedor Strok (Федор Владимирович Строк)

[https://github.com/EgorDudyrev/OSDA_course GitHub repo of the course]

[https://arxiv.org/abs/1908.11341 Students book on Arxiv]

[https://t.me/+LaVJXmxCUCJhZmFi Telegram channel (this one)]

[https://t.me/+pDCm5PbTeNxiMDdi Telegram chat]

== Schedule ==

{| class="wikitable"
|-
! 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 ==

{| class="wikitable"
|-
! Task description !! Source !! Deadline
|-
| Solve the problems given at the end of the first lecture. The exact problems to solve are: 3-6 || [https://github.com/addicted-by/hse_courses/raw/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture1_ordered_sets_ds.pdf Lecture 1] || 20.09.22
|}

== Lectures ==

{| class="wikitable"
|-
! 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. || [https://github.com/addicted-by/hse_courses/raw/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture0_ordered_sets_ds.pdf Click] || Missing|| [https://github.com/addicted-by/hse_courses/blob/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture0_ordered_sets_ds.pdf 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. || [https://github.com/addicted-by/hse_courses/raw/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture1_ordered_sets_ds.pdf Click] || [https://github.com/addicted-by/hse_courses/raw/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture1_ordered_sets_ds_ru.pdf Click] || [https://github.com/addicted-by/hse_courses/blob/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture1_ordered_sets_ds.pdf Click]|| 37p|| 20-30 min read

|-
| Lecture 2 || 27.09.22 || Quasi order, partial order. Topological sorting. Dushnik-Miller theorem. Applications. || [https://github.com/addicted-by/hse_courses/raw/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture2_ordered_sets_ds.pdf Click] || [https://github.com/addicted-by/hse_courses/raw/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture2_ordered_sets_ds_ru.pdf Click] || [https://github.com/addicted-by/hse_courses/blob/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture2_ordered_sets_ds.pdf Click]|| 54p || 30-40 min read

|-
| Lecture 3 || 11.10.22 || Lattices and closures. Semilattices. Distributivity and modularity. || [https://github.com/addicted-by/hse_courses/raw/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture3_ordered_sets_ds.pdf Click] || Missing || [https://github.com/addicted-by/hse_courses/blob/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture3_ordered_sets_ds.pdf Click]|| 44p || 30-40 min read

|-
| Lecture 4 || 11.10.22 || Introduction to Formal Concept Analysis. Concept lattice and implications. || [https://github.com/addicted-by/hse_courses/raw/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture4_ordered_sets_ds.pdf Click] || Missing || [https://github.com/addicted-by/hse_courses/blob/main/1st_year/term1/module1/ordered_sets_data_science/lectures/lecture4_ordered_sets_ds.pdf Click]|| 35p || 24-26 min read
|}

== Seminars ==

{| class="wikitable"
|-
! 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.

Ordered Sets in Data Analysis (2022) - История изменений

imported>Asryabykin: добавил лекции по осда