* Students have the knowledge on the fundamental concepts of linear algebra, including vectors, matrices, systems of linear equations, eigenvalues, eigenvectors, linear transformations, and orthogonality.
* Students will be able to look at the same problem from different angles and they will learn to switch their point of view (from geometric to algebraic and vice versa).
* Students will recognize that linear algebra can be applied to problems from different disciplines.
* Students will obtain the insight that various seemingly different questions all can boil down to the same mathematical problem of solving a system of equations.
* Students can argue (in a sometimes abstract way) which approach may or may not work.
* Students are able to give clear arguments to support their solutions in a mathematically correct manner.
* Students will be able to understand and write the language of linear algebra.

Linear Algebra is the second basic mathematics course of the program. Similar to Calculus, it builds on high school mathematics and prepares you for advanced engineering courses that focus on computational skills and techniques. This course focusses on the theory of linear transformations on linear vector spaces. The underlying fundamental concepts of matrices and vectors are explained and applied to problem solving. Rigorous reasoning with these concepts with attention to precision in formulation is a key aspect of the course. You gain insight into algebraic and geometric concepts including vectors, matrices, linear transformations, eigenvalues and eigenvectors, inner products and orthogonality. You learn to perform basic algorithmic calculations (matrices, equations…) and solve more abstract algebraic problems. You also gain insights into the applications of linear algebra in several engineering and scientific disciplines. (5.0 ECTS)

David C. Lay, Steven R. Lay, and Judi J. McDonald (2021). Linear Algebra and Its Applications (Sixth Edition). ISBN: 978-1-292-35121-6.

No prerequisites are required.

Linearity and linear independence, matrix algebra, determinants, vector spaces, eigenvalues and eigenvectors, orthogonality