The introduction course to math for data science, MSU Fall 2021 - Spring 2022

Date	Lectures	Practice sessions
11.11.2021	Lecture 1. Floating point numbers, veсtors and vector norms [GitHub]	Seminar 1
18.11.2021	Lecture 2. Matrices, their properties and norms. Lowrank approximation, SVD and applications. [GitHub]	Seminar 2
25.11.2021	Lecture 3. Linear systems. LU decomposition. [GitHub]	Seminar 3
02.12.2021	Lecture 4. Condition number. QR decomposition. Linear least-squares problem. [GitHub]	Seminar 4
09.12.2021	Lecture 5. Sparse matrices and LU for them [GitHub]	Seminar 5
16.12.2021	Lecture 6. Eigendecomposition. Schur theorem and QR algorithm [GitHub]	Seminar 6
23.12.2021	Lecture 7. Introduction to numerical methods for linear systems [GitHub]	Seminar 7
10.02.2022	Lecture 8. Intro to optimization methods. Random search and gradient descent [GitHub]	Seminar 8
17.02.2022	Lecture 9. Gradient descent and heavy-ball method [GitHub]	Seminar 9
03.03.2022	Lecture 10. Accelerated gradient method and Newton method [GitHub]	Seminar 10
10.03.2022	Lecture 11. Intro to stochastic gradient methods [GitHub]	Seminar 11
17.03.2022	Lecture 12. Quasi-Newton methods [GitHub]	Seminar 12

References

For beginners
More advanced books
- Gene H. Golub, Charles. F. Van Loan, "Matrix computations" (4th edition)
- Lloyd N. Trefethen and David Bau III, "Numerical Linear Algebra"
- Eugene. E. Tyrtyshnikov, "Brief introduction to numerical analysis"
- James W. Demmel, "Numerical Linear Algebra"