# Mathematics for Machine Learning – Linear Algebra

## Course Description

Mathematics for Machine Learning - Linear Algebra is an essential course for students looking to develop a comprehensive understanding of linear algebra's role in the world of machine learning. In this course, students will delve into the mathematical concepts and principles that underlie machine learning algorithms and gain an in-depth understanding of how linear algebra is used in the field. Linear algebra is the branch of mathematics that deals with vector spaces, matrices, and linear transformations. It forms the backbone of many machine learning algorithms, including principal component analysis, linear regression, and support vector machines. Therefore, understanding the basics of linear algebra is crucial for anyone interested in pursuing a career in machine learning. The course covers a broad range of topics, including matrix operations, determinants, eigenvectors, and eigenvalues. Students will learn how to perform matrix multiplication, matrix inversion, and how to solve systems of linear equations using matrix methods. They will also learn how to calculate eigenvalues and eigenvectors, which are critical in many machine learning applications. The course emphasizes the application of linear algebra in machine learning, providing students with practical examples of how linear algebra is used in the field. Students will learn how to use linear algebra to model data, identify patterns, and make predictions. They will also learn how to use linear regression to model relationships between variables and how to use principal component analysis to reduce the dimensionality of large datasets. The course is designed to be accessible to students with a basic understanding of calculus and linear algebra. It is delivered through a combination of lectures, hands-on exercises, and problem sets. Students will have the opportunity to work on real-world machine learning problems and gain practical experience with the tools and techniques used in the field. Upon completion of the course "Mathematics for Machine Learning - Linear Algebra", students will have a strong foundation in linear algebra and its applications in machine learning. They will be able to apply their knowledge to a wide range of machine learning problems and will be well-prepared to pursue further studies in the field. Author: Imperial College London, Dr David Dye, Dr Sam Cooper