Бакалавриат
2025/2026
Первые шаги в линейной алгебре для машинного обучения
Статус:
Курс по выбору (Психология)
Кто читает:
Департамент психологии
Где читается:
Факультет социальных наук
Когда читается:
4-й курс, 3 модуль
Онлайн-часы:
20
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Колачев Никита Игоревич
Язык:
английский
Кредиты:
3
Контактные часы:
6
Course Syllabus
Abstract
The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data analysis. You will learn the fundamentals of working with data in vector and matrix form, acquire skills for solving systems of linear algebraic equations and finding the basic matrix decompositions and general understanding of their applicability. This course is suitable for you if you are not an absolute beginner in Matrix Analysis or Linear Algebra (for example, have studied it a long time ago, but now want to take the first steps in the direction of those aspects of Linear Algebra that are used in Machine Learning). Certainly, if you are highly motivated in study of Linear Algebra for Data Sciences this course could be suitable for you as well.
Learning Objectives
- The goal of the course is to apply matrix analysis to machine learning. We study the aspects of linear algebra that are used in data Science.
Expected Learning Outcomes
- Be able to apply the concepts of Euclidean space in the least squares method for finding approximate solutions of linear systems and in the linear regression model based on it. Know the core of the most common linear classifier, called the support vector machine.
- Know methods for finding linear system solutions based on Gaussian exceptions and LU decompositions. Be able to use Python code for matrix calculations.
- Know the fundamental concepts of linear algebra, namely: vector spaces, linear independence and basis, matrix rank, properties of a set of solutions for a system of linear equations. Be able to apply this theory to the processing of scanned documents.
Course Contents
- Full rank decomposition and systems of linear equations
- Euclidean spaces
- Systems of linear equations and linear classifier
Interim Assessment
- 2025/2026 3rd module0.25 * Quiz: Life expectancy prediction quiz + 0.25 * Quiz: Week 1 + 0.25 * Quiz: Week 2 + 0.25 * Quiz: Week 3
Bibliography
Recommended Core Bibliography
- 9781491962992 - Bengfort, Benjamin; Bilbro, Rebecca; Ojeda, Tony - Applied Text Analysis with Python : Enabling Language-Aware Data Products with Machine Learning - 2018 - O'Reilly Media - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1827695 - nlebk - 1827695
- A Tutorial on Machine Learning and Data Science Tools with Python. (2017). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E5F82B62
Recommended Additional Bibliography
- Alpaydin, E. (2014). Introduction to Machine Learning (Vol. Third edition). Cambridge, MA: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=836612