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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

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