Mathematics for computer vision

The course is devoted to the systematization of the mathematical background of the students necessary for the successful mastering of educational disciplines in the field of computer vision. The course includes sections of mathematical analysis, probability theory, linear algebra.
Aim of the course:
• Systematization of the mathematical background
• Preparation for the use of mathematical knowledge in the professional activities of a specialist in the field of
computer vision.
Practical Learning Outcomes expected:
• Mastering practical skills in mathematics
• The solution of mathematical problems that are encountered in the practical work of a specialist in the field of computer vision.

This Course is part of HSE University Master of Computer Vision degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/r381p.

• Vectors
• In this module we provide you with the most important concepts about vectors and vector spaces which are widely used in the area of machine learning and computer vision.
• Matrices
• Matrices play key role in various computer vision algorithms. In this section we show different operations on matrices and also give the information about different theoretical features of matrices that are necessary for the practical use.
• Functions
• This module contains the fundamental concepts about functions, such as continuity, differentiation and integration. They are extremely important for various machine learning methods, for example, in training procedures (optimization of parameters).
• Project week