M1110D Linear algebra for data science

Faculty of Science
Autumn 2026
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. Mgr. Ondřej Klíma, Ph.D. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (assistant)
Mgr. Markéta Barać Makarová (seminar tutor)
Guaranteed by
doc. Mgr. Ondřej Klíma, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: doc. Mgr. Ondřej Klíma, Ph.D.
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 12:00–13:50 M4,01024
  • Timetable of Seminar Groups:
M1110D/01: Thu 18:00–19:50 M2,01021, M. Barać Makarová
Prerequisites
!NOW(MB141 Seminar from algebra)
Secondary school mathematics.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Abstract

The course covers the fundamentals of linear algebra and matrix calculus, with an emphasis on concepts and algorithms directly applicable to data science.

Learning outcomes

At the end of this course, students should be able to: efficiently solve systems of linear equations, perform matrix operations, and apply specific matrix decompositions; analyze eigenvalues and eigenvectors, and model the behavior of discrete linear processes; and identify and exploit the properties of special types of matrices to model real-world problems.

Key topics

Complex numbers. Systems of linear equations. Gaussian elimination. Matrix operations. Determinant. Vector spaces and subspaces. Inner products. Linear transformations. Eigenvalues and eigenvectors. Linear processes. Invertible, symmetric, orthogonal, stochastic, and other special matrices. Specific matrix decompositions.

Study resources and literature
    recommended literature
  • J. Slovák, M. Panák, M. Bulant: Matematika drsně a svižně
  • J. A. Fessler, R. R. Nadakuditi: Linear Algebra for Data Science, Machine Learning, and Signal Processing
Approaches, practices, and methods used in teaching
Lecture including a demonstrative solution of typical problems. Seminar including active solving of problems. Homework.
Method of verifying learning outcomes and course completion requirements
During the semester, weekly homework will be assigned as online questionnaires in the IS (12 points), and active participation in seminars will also be assessed (8 points). Out of the total 20 points, a minimum of 10 points is required to qualify for the final exam. Any points earned above the mandatory 10 will be carried over as bonus points towards the overall evaluation. The final exam consists of a written part (60 points) and an oral part (40 points). To successfully complete the course (with a minimum grade of E), students must obtain at least 30 points on the written exam and at least 50 points overall.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2026/M1110D