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The Institute
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Math402

Linear Algebra 2

Online
Nov 29, 2021 - Dec 17, 2021
In the Linear Algebra 2 course, students to understand and learn more advanced topics such as Machine Learning, Optimization Theory, Theory of Control and others.
Online
Nov 29, 2021 - Dec 17, 2021
Mikhail Romanov

Faculty

Mikhail Romanov

Senior Machine Learning Engineer, Yandex, Expert

Course length

3 weeks

Duration

3 hours
per day

Total hours

45 hours

Credits

4 ECTS

Language

English

Course type

Online

Fee for single course

€1500

Fee for degree students

€750

Skills you’ll learn

Linear AlgebraComplex Vectors and MatricesMatrix FactorizationsApplications of Linear AlgebraTensors and Operations with TensorsDeterminantsFourier Transform
OverviewCourse outlineCourse materialsPrerequisitesMethod & grading

Overview

Linear Algebra is one of the core mathematical fields. In the beginning of the 20th Century, the demand in this area has grown tremendously with the rise of Quantum Mechanics. Since then, it has found numerous applications in the majority of the Natural Sciences (Physics, Chemistry, Electronics, etc.) as well as in Scientific Computing (Optimization Theory, Theory of Control, Machine Learning, Computer Vision, Signal Processing, etc.).

This course is a successor of a Linear Algebra 1 course.

This course is a must-know for areas such as Machine Learning, Optimization Theory, Theory of Control, Deep Learning and Neural Networks (these are the courses that may demand this course as a prerequisite).

Learning highlights

  • Analysing the matrices, linear transforms and bilinear forms
  • Finding the eigen values and vectors and singular values and vectors
  • Matrix decompositions and their properties
  • Linear Operators for discrete functions, Hilbert Spaces, Fourier Transforms, Discrete Fourier Transforms
  • Tensors (covariant and contravariant) and operations with Tensors

Course outline

15 classes

Dive into the details of the course and get a sense of what each class will cover.
Monday
Tuesday
Wednesday
Thursday
Friday
Monday
1

Session 1

Rehearsal. Linear transforms and the geometric meaning of linear transforms.

Tuesday
2

Session 2

Seminar: Rehearsal

Wednesday
3

Session 3

Determinants. Properties of Determinants. Permutations and Cofactors. Cramer’s rule, Inverse Matrix. Determinant as volume.

Thursday
4

Session 4

Seminar: Determinants

Friday
5

Session 5

Eigenvalues and Eigenvectors. Equation for Eigenvectors. Matrix diagonalization.

Symmetric matrices. Positive and negative definite matrices.

Monday
6

Session 6

Seminar: Eigenvectors and Eigenvalues

Tuesday
7

Session 7

Covariance Matrix. Singular Value Decomposition. Properties. Diagonalization and Pseudoinverse.

Wednesday
8

Session 8

Seminar: SVD

Thursday
9

Session 9

Complex Vectors and Matrices. Hermitian and Unary Matrices.

Friday
10

Session 10

Seminar: Complex vectors and matrices

Monday
11

Session 11

Hilbert spaces. Generalization of Scalar Product. Fourier Transform. Discrete Fourier Transform.

Tuesday
12

Session 12

Seminar: Fourier Transform

Wednesday
13

Session 13

Linear Transformation. Matrix of a linear transformation. Rotation Matrix. Change of Basis.

Tensors and operations with Tensors. Einstein’s rule. Vectors and co-vectors.

Thursday
14

Session 14

Seminar: Tensors.

Applications of linear algebra in Science

Friday
15

Session 15

Final exam

Methodology

Our sessions consist of two parts: a lecture session with slides and theoretical materials and a seminar session with problem solving. The seminar sessions will include both math problems and programming tasks.

The knowledge is the amount of problems that you have solved. Thus, I will be marking your homework assignments and practical tasks. Class activity will be rewarded with extra points.

Grading

The final grade will be composed of the following criteria:
70% - Homework and lab projects
30% - Final Exam
Mikhail Romanov

Faculty

Mikhail Romanov

Senior Machine Learning Engineer, Yandex, Expert

Mikhail Romanov, PhD, is a deep learning researcher and engineer. His experience includes deep learning for production, scientific computing and research, accompanied by teaching mathematics and machine learning in general.

His academic experience includes teaching courses at MIPT, HSE, Harbour Space Universities and online platforms. As a researcher, he has conducted research at the Technical University of Denmark, Mail.ru, Samsung Research, Quantori, and Yandex. In his research, his main areas of interest are depth estimation, optical flow, optimisation of neural networks, multi-task learning, self-supervised learning, LLMs and diffusion models. He has published papers on tomography, deep learning, scientific computing, computer vision, generative AI, and diffusion models.

See full profile

Apply for this course

Snap up your chance to enroll before all spaces fill up.

Linear Algebra 2

by Mikhail Romanov

Total hours

45 Hours

Dates

Nov 29 - Dec 17, 2021

Fee for single course

€1500

Fee for degree students

€750

How to secure your spot

Complete the form below to kickstart your application

Schedule your Harbour.Space interview

If successful, get ready to join us on campus

FAQ

Will I receive a certificate after completion?

Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.

Do I need a visa?

This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.

Can I get a discount?

Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.