Welcome to the repository for the Linear Algebra for Data Science course, part of the Data Science master's program at Higher School of Economics (HSE). This course is taught by Dmitri Piontkovski, a leading expert in the field. Here, you will find all the essential materials and resources needed to succeed in this course.
The course consists of lectures and seminars. Materials for subsequent lectures and seminars will be made available as the course progresses.
| β | Title | Lecture | Seminar | Homework |
|---|---|---|---|---|
| 1 | Intro, Pseudoinverse and Skeletonization | π | π | π |
| 2 | Pseudosolutions | π | π | |
| 3 | Matrix Decompositions | π | π | π |
| 4 | Interpolation problem, Splines and BΓ©zier curves | π | π | |
| 5 | Metric spaces and Normed vector spaces | π | ||
| 6 | Chebyshev polynomials | π | ||
| 7 | Norms in finite dimension vector spaces | π | π | |
| 8 | Matrix norms | π | π | |
| 9 | Low rank approximation | π | π | |
| 10 | Approximate systems | π | π | |
| 11 | Iteration methods | π | π | |
| 12 | Peron-Frobenius, Pagerank | TBA | π | |
| 13 | Functions of matrices | TBA | TBA | |
| 14 | TBA | TBA | TBA |
Π‘ourse participants are invited to make a talk with their own projects. Here is a sample list of projects. If you choose of create a project, please fill the table.
If you have questions or need assistance with the course, you can reach out to Professor Dmitri Piontkovski.
Authors of this Repository:
We wish you a successful and rewarding experience in the Linear Algebra for Data Science course! Good luck with your studies.