Linear Algebra for Data Science (2022)

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Версия от 03:25, 22 октября 2022; Asryabykin (обсуждение | вклад)

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General information

One semester course. 6 credits.

Lecturer: Dmitri Piontkovski (Дмитрий Игоревич Пионтковский)

Class teacher: Vsevolod Chernyshev (Всеволод Леонидович Чернышев)

Telegram channel

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

Homeworks

Type Date Topics Download materials Read Materials Tasks Time to completion Deadline Download Solution Read Solution
Optional 14.09.22 Pseudoinverse matrices. Skeletonization. Click Click 4 15-30 min 16.09.22 Click Click
Soon 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 Click
Lecture 2 16.09.22 Pseudosolutions and its applications. Linear Regression. Click Click 3p (v1.0) 10 min read Click
Lecture 3 23.09.22 Approximation. Interpolation problem. Polynomial interpolation.
Hermitian interpolation. Splines. Bézier curves and splines.
Click Click 5(v1.5) 10 min read Click
Lecture 4 07.10.22 Metric axioms. Metric spaces. Norms. Normed linear spaces. Click Click 4(v1.5) 15 min read Click
Lecture 5 14.10.22 Norms. Minkovski's theorem. Euclidian space. Click Click 1(v0) 12 min read Click
Lecture 6 21.10.22 Chebyshev polynomials of the first kind. Chebyshev polynomials of the second kind. Click Click 1(v0) 12 min read Click
Lecture 7 soon soon soon soon soon soon soon

Seminars

All seminars you will find here

Seminar Date Topics Download materials Read Materials Pages Reading time GitHub (for changes)
Seminar 1 09.09.22 Pseudoinverse matrices. Skeletonization. Singular value decomposition (SVD) Click Click 3p 9 min read GitHub link
Seminar 2 16.09.22

References

Main literature

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.

Contacts

Polina Moskvicheva

Aleksey Ryabykin Github