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 part of the Mathematics for Data Science Specialization
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Mathematics for Data Science SpecializationHSE UniversityAbout this Course
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Intermediate Level
Some background in Python programming language and algebra.
Approx. 14 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Some background in Python programming language and algebra.
Approx. 14 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Offered by
HSE University
HSE University is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more.
Start working towards your Master's degree
This course is part of the 100% online Master of Data Science from HSE University.
If you are admitted to the full program, your courses count towards your degree learning.
Syllabus - What you will learn from this course
5 hours to complete
Systems of linear equations and linear classifier
In the first week we provide an introduction to multi-dimensional geometry and matrix algebra. After that, we study methods for finding linear system solutions based on Gaussian eliminations and LU-decompositions. We illustrate the methods with Python code examples of matrix calculations.
5 hours to complete
15 videos (Total 118 min), 3 readings, 2 quizzes
2 hours to complete
Full rank decomposition and systems of linear equations
The second week is devoted to getting to know some fundamental notions of linear algebra, namely: vector spaces, linear independence, and basis. Next, we will discuss what a rank of a matrix is, and how it could help us decompose a matrix. In addition, we will talk about the properties of a set of solutions for a system of linear equations. At the end of this week we will apply this theory to a scanned document processing.
2 hours to complete
14 videos (Total 86 min)
2 hours to complete
Euclidean spaces
In the third week, we firstly introduce coordinates in an abstract vector space. This allows us to apply the usual matrix arithmetic to abstract vectors. Next, we discuss the concept of Euclidean space which allows us to measure distances and angles in vector spaces. Then we use these measures in the least squares method to find approximate solutions of linear systems and in the linear regression model based on it. Finally, we describe the core of the most common linear classifier called Support Vector Machine.
2 hours to complete
10 videos (Total 85 min)
4 hours to complete
Final Project
In this week we will apply the acquired knowledge about linear regression and SVM models in this final project.
4 hours to complete
1 video (Total 2 min), 1 reading, 2 quizzes
Reviews
TOP REVIEWS FROM FIRST STEPS IN LINEAR ALGEBRA FOR MACHINE LEARNING
by AGJul 24, 2020
with great assignments,one could get relation of LA with computer science.For naive english it could hurdles at first.but nice content.must for intermediatory
by EADec 30, 2020
This is a well designed course, with the right balance of theory and practice. The quizzes and programming assignments reinforce the material effectively.
by DCFeb 29, 2020
The material was very well presented, and the exercises were helpful for learning
by JMApr 12, 2021
Excellent course. It explains really good the Linear Algebra fundamentals for ML
About the Mathematics for Data Science Specialization
Behind numerous standard models and constructions in Data Science there is mathematics that makes things work. It is important to understand it to be successful in Data Science. In this specialisation we will cover wide range of mathematical tools and see how they arise in Data Science. We will cover such crucial fields as Discrete Mathematics, Calculus, Linear Algebra and Probability. To make your experience more practical we accompany mathematics with examples and problems arising in Data Science and show how to solve them in Python.
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