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# Linear Algebra II: Matrices and Linear Transformations

### Overview

A strong foundation in mathematics is critical for success in all science and engineering disciplines. Whether you want to make a strong start to a masterâ€™s degree, prepare for more advanced courses, solidify your knowledge in a professional context or simply brush up on fundamentals, this course will get you up to speed.

In many engineering masterâ€™s programs, you need to be familiar with linear algebra. This course will enable you to review the relevant topics.

This course focuses on matrices and linear transformations. Topics covered include matrix algebra, determinants, eigenvalues and eigenvectors, diagonalization and singular value decomposition. The course will help you refresh your knowledge, test your skills and review the relations between the many concepts in linear algebra.

The linear algebra courses within this series will offer you an overview of this branch of mathematics common to most engineering bachelorâ€™s programs. They provide enough depth to cover the linear algebra you need to succeed in your engineering masterâ€™s/profession in areas such as computer graphics, systems and control, machine learning, quantum computing and more.

This is a review course
This self-contained course is modular, so you do not need to follow the entire course if you wish to focus on a particular aspect. As a review course you are expected to have previously studied or be familiar with most of the material. Hence the pace will be higher than in an introductory course.

This format is ideal for refreshing your bachelor level mathematics and letting you practice as much as you want. Through the Grasple platform, you will have access to plenty of exercises and receive intelligent, personal and immediate feedback.

### Syllabus

Week 1:

• matrix inversion

Week 2:

• linear transformations
• standard matrix of a linear transformation
• examples of linear transformations in geometry

Week 3:

• determinants
• methods to find determinants
• applications of determinants

Week 4:

• eigenvalues and eigenvectors
• eigenspaces
• characteristic polynomials
• complex eigenvalues
• multiplicities of eigenvalues

Week 5:

• diagonalization
• similarity transformations
• coordinate transformations

Week 6:

• symmetric matrices
• singular value decomposition

### Taught by

Bart van den Dries, Marleen Keijzer and Iris Smit

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