Ordered Sets in Data Analysis (2022) — различия между версиями

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м
м (добавил лекции по осда)
 
(не показаны 2 промежуточные версии этого же участника)
Строка 5: Строка 5:
  
 
Class teacher: Fedor Strok (Федор Владимирович Строк)
 
Class teacher: Fedor Strok (Федор Владимирович Строк)
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 +
[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)]
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 +
[https://t.me/+pDCm5PbTeNxiMDdi Telegram chat]
  
 
== Schedule ==
 
== Schedule ==
Строка 19: Строка 27:
 
== Grading system ==
 
== Grading system ==
  
−
soon
+
'''<span style="color:red">Final Grade</span>''' = 0.45 * '''<span style="color:green">first_module</span>''' + 0.55 * '''<span style="color:green">second_module</span>'''
 +
 
 +
'''<span style="color:green">first_module</span>''' = 0.5 * '''''avg''''' (homeworks) + 0.5 * '''test''' (at the end of the module)
 +
 
 +
'''<span style="color:green">second_module</span>''' = 0.3 * '''''avg''''' (homeworks) + 0.4 * '''big homework''' + 0.3 * '''test''' (at the end of the module)
 +
----
 +
'''<span style="color:blue">Homeworks</span>''' -- assignments given after each lecture;
 +
 
 +
'''<span style="color:blue">Big Homework</span>''' -- 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 ==
 
== Lectures ==
Строка 25: Строка 56:
 
{| class="wikitable"
 
{| class="wikitable"
 
|-
 
|-
−
! Lecture !! Date !! Topics !! Download materials !! Read Materials !! Pages !!  Reading time !! GitHub (for changes)
+
! Lecture !! Date !! Topics !! Download materials !! Russian Materials !! Read Materials !! Pages !!  Reading time  
 
|-
 
|-
−
| soon || soon || soon || soon || soon || soon || soon || soon
+
| 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
 
|}
 
|}
  
Строка 34: Строка 77:
 
{| class="wikitable"
 
{| class="wikitable"
 
|-
 
|-
−
! Seminar !! Date !! Topics !! Download materials !! Read Materials !! Pages !!  Reading time !! GitHub (for changes)
+
! Seminar !! Date !! Topics !! Download materials !! Read Materials !! Pages !!  Reading time
 
|-
 
|-
−
| soon || soon || soon || soon || soon || soon || soon || soon
+
| soon || soon || soon || soon || soon || soon || soon
 
|}
 
|}
  
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===[https://github.com/ References]===
+
=== References ===
  
 
===='''<span style="color:red">Main literature</span>'''====
 
===='''<span style="color:red">Main literature</span>'''====
−
* soon
+
* 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.
  
 
===='''<span style="color:green">Additional literature</span>'''====
 
===='''<span style="color:green">Additional literature</span>'''====
−
* soon
+
* 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.

Текущая версия на 01:53, 24 октября 2022

General information

One semester course. 6 kredits.

Lecturer: Sergey Kuznetsov (Сергей Олегович Кузнецов)

Class teacher: Fedor Strok (Федор Владимирович Строк)

GitHub repo of the course

Students book on Arxiv

Telegram channel (this one)

Telegram chat

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.
Источник — «http://wiki.cs.hse.ru/index.php?title=Ordered_Sets_in_Data_Analysis_(2022)&oldid=73518»