Math113BKK
Linear Algebra
Faculty
Mikhail Romanov
Senior Machine Learning Engineer, Yandex, Expert
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
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 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
Course outline
15 classes
Session 1
Vectors and vector operations. Lengths and Dot products. Orthogonality. Orthogonalization procedure.
Session 2
Seminar
Session 3
Matrices and matrix operations. Systems of linear equations in matrix form. Gauss Algorithm. LU decomposition. Inverse matrices.
Solving sysmems of linear equations with insufficient amount of equations. Matrix kernel.
Session 4
Seminar
Session 5
Determinants. Properties of Determinants. Permutations and Cofactors. Cramer’s Rule, Inverse Matrix. Determinant as volume.
Session 6
Seminar: Determinants
Session 7
Eigenvalues and Eigenvectors. The equation for Eigenvectors. Matrix diagonalisation. Symmetric matrices. Positive and negative definite matrices.
Session 8
Seminar: EigenValues and EigenVectors
Session 9
Covariance Matrix. Singular Value Decomposition. Properties. Diagonalisation and Pseudoinverse.
Session 10
Singular Value Decomposition: digging deeper
Session 11
Seminar: Singular Value Decomposition
Session 12
Complex Vectors and Matrices. Hermitian and Unary Matrices.
Session 13
Hilbert spaces. Generalisation of Scalar Product. Fourier Transform. Discrete Fourier Transform.
Session 14
Tensors
Session 15
Final exam
Course materials
Books
Media
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
Grading
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 profileApply for this course
Linear Algebra
by Mikhail Romanov
Total hours
45 Hours
Dates
May 27 - Jun 14, 2024
Fee for single course
€1500
Fee for degree students
€750
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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.