Module/Course Title: Linear Algebra
Module course code KOMS120301	Student Workload 119 hours	Credits 3 / 4.5 ETCS	Semester 3	Frequency Odd Semester	Duration 16
1	Type of course Core Study Courses	Contact hours 40 hours of face-to-face (theoretical) class activity	Independent Study 48 hours of independent activity 48 hours of structured activities	Class Size 30
2	Prerequisites for participation (if applicable) -
3	Learning Outcomes Students can use mathematical and logical concepts that support the scientific fields of computer science Students can solve systems of linear equations Students can use matrix algebra and the related matrices to linear transformations Students can explain the concept of vectors and matrices Students can explain vector space, basis, dimension and product space Student can compute and use determinants Student can compute and use eigenvectors and eigenvalues Student can determine and use orthogonality
4	Subject aims/Content Linear algebra is a branch of mathematics that deals with the study of vector spaces and linear transformations. What is studied in this course is a system of linear equations, analyzing geometric transformations, and understanding the properties of vectors and matrices. The study of linear algebra involves examining how these vectors and matrices interact and transform in various mathematical contexts. One of the basic concepts in linear algebra is the concept of vector spaces. Vector spaces allow exploration of concepts such as linear freedom, range, and basis. Moreover, linear transformation is another important aspect of linear algebra which describes how vectors change under certain mathematical operations. They can be represented by matrices, where each column of the matrix corresponds to a certain basis vector image. On the other hand, eigenvalues and eigenvectors play an important role in linear algebra. They are related to linear and matrix transformations, representing special vectors and scalars that remain unchanged, except for scaling, under the action of transformations or matrices. Study Material Introduction to Linear Equations Gauss and Gauss Jordan Elimination Matrix and  matrix arithmetic Basic Matrices, Methods of finding Inverse, Diagonal, Triangle and Symmetrical Matrices Determinant of a matrix Vector, Vector Arithmetic and Dot Product Cross product vectors, determinants for cross products, lines and planes in 3D space - Vector space sub-vector spaces Bases and dimensions Inner Product Space Orthonormal basis and Gram-Schmidt method Eigen Space Linear Transformation -
5	Teaching methods Synchronous: face-to-face/online meeting Asynchronous: Modul delivered through e-learning
6	Assesment Methods Attendance and participation
7	This module/course is used in the following study programme/s as well Computer Science Study Programme
8	Responsibility for module/course Ni Wayan Marti, S.Kom., M.Kom NIDN : 0028117701
9	Other Information Howard Anton, and Chris Rorres, 2000, Linear Algebra Elementary, Applications Version, Eight Edition, John Wiley and Sons, Inc., New York. Aldo G. S. Ventre. (2023). Calculus and Linear Algebra. Fundamentals and Applications. Springer David C. Lay, Steven R. Lay, Judi J. McDonald. (2016). Linear Algebra and Its Applications. Pearson Education Mark J. DeBonis. (2022). Introduction To Linear Algebra: Computation, Application, and Theory. Boca Raton:CRC Press Lina Oliveira. (2022). Linear Algebra. Boca Raton:CRC Press