Linear Algebra for Data Science (2022) — различия между версиями
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| − | | Lecture 1 || 09.09.22 || Distinctive features of applied linear algebra. <br> Problems with real data. Pseudoinverse matrices. Skeletonization. || [https://github.com/addicted-by/hse_courses/raw/main/1st_year/term1/module1/linear_algebra_data_science/lecture1_linear_algebra_for_ds.pdf Click] || [https://github.com/addicted-by/hse_courses/blob/main/1st_year/term1/module1/linear_algebra_data_science/lecture1_linear_algebra_for_ds.pdf Click] || 3p || 7 min read || [https://github.com/addicted-by/hse_courses/tree/main/1st_year/term1/module1/linear_algebra_data_science/lectures/lecture1.tex GitHub link] | + | | Lecture 1 || 09.09.22 || Distinctive features of applied linear algebra. <br> Problems with real data. Pseudoinverse matrices. Skeletonization. || [https://github.com/addicted-by/hse_courses/raw/main/1st_year/term1/module1/linear_algebra_data_science/lectures/lecture1_linear_algebra_for_ds.pdf Click] || [https://github.com/addicted-by/hse_courses/blob/main/1st_year/term1/module1/linear_algebra_data_science/lectures/lecture1_linear_algebra_for_ds.pdf Click] || 3p || 7 min read || [https://github.com/addicted-by/hse_courses/tree/main/1st_year/term1/module1/linear_algebra_data_science/lectures/lecture1.tex GitHub link] |
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| Lecture 2 || 16.09.22 || || || || || || | | Lecture 2 || 16.09.22 || || || || || || | ||
Версия 23:54, 11 сентября 2022
Содержание
General information
One semester course. 6 credits.
Lecturer: Dmitri Piontkovski (Дмитрий Игоревич Пионтковский)
Class teacher: Vsevolod Chernyshev (Всеволод Леонидович Чернышев)
Schedule
| Type | Day of week | Time | Place |
|---|---|---|---|
| Lectures | Friday | 18:10-19:30 | S224, Pokrovsky Blvd. 11 (Покровский б-р 11) |
| Seminars | Friday | 19:40-21:00 | S224, Pokrovsky Blvd. 11 (Покровский б-р 11) |
Grading system
Final Grade = 0.5 * Test1 + 0.5 * Test2 + Bonus (for a talk, ≤ 5) + Bonus (for classes, ≤1..2)
Tests: unique for everyone
Topics on which you can prepare a talk: on your own (based on your experience) or from list: will be published soon
Lectures
All lectures you will find here
| Lecture | Date | Topics | Download materials | Read Materials | Pages | Reading time | GitHub (for changes) |
|---|---|---|---|---|---|---|---|
| Lecture 1 | 09.09.22 | Distinctive features of applied linear algebra. Problems with real data. Pseudoinverse matrices. Skeletonization. |
Click | Click | 3p | 7 min read | GitHub link |
| Lecture 2 | 16.09.22 |
Seminars
| Seminar | Date | Topics | Materials | Reading time | GitHub (for changes) |
|---|---|---|---|---|---|
| Seminar 1 | 09.09.22 | Pseudoinverse matrices. Skeletonization. Singular value decomposition (SVD) | soon | GitHub link | |
| Seminar 2 | 16.09.22 |
References
Main literature
- Тыртышников Е. Е. Матричный анализ и линейная алгебра. Учебное пособие. (2007)
- Беклемишев Д.В., Дополнительные главы линейной алгебры, СПБ, изд. Лань, 2008
- Шевцов Г.С. Линейная алгебра: теория и прикладные аспекты: Учеб. пособие. М.: Финансы и статистика, 2003 (или другой год издания). 576 с
- Olver, P.J., and Shakiban, C. Applied linear algebra. 2nd edition. Springer, 2018
- R. Horn and C. Jonson. Matrix analysis. 2nd edition. Cambridge Univ. Press, 2013
Additional literature
- Винберг Э.Б., Курс алгебры, М., изд. МГУ, 2002 (и последующие издания);
- Бахвалов Н., Жидков Н., Кобельков Н., Численные методы, М., изд. Бином, 2003 (или другой год издания);
- Колмогоров А.Н., Фомин С.В., Элементы теории функций и функционального анализа, М., изд. Наука, 1976 (или другой год издания);
- Aleskerov F., Ersel H., Piontkovski D. Linear Algebra for Economists. Berlin—Heidelberg, Springer, 2011;
- Bryan, K. and Leise, T., 2006. The $25,000,000,000 eigenvector: The linear algebra behind Google. SIAM review, 48(3), pp.569-581;
- D. Cox, J. Little, and D. O’Shea. Ideals, varieties, and algorithms: an introduction to computational algebraic geometry and commutative algebra. Springer Science & Business Media, 2013.
