Syllabus for Applied Linear Algebra for Data Science

Tillämpad linjär algebra för dataanalys

A revised version of the syllabus is available.


  • 7.5 credits
  • Course code: 1TD060
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1F, Data Science A1F

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2021-03-04
  • Established by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2021
  • Entry requirements:

    120 credits. Computer Programming II or Programming, Bridging Course. Linear Algebra II. Scientific Computing II or Scientific Computing, Bridging Course or Statistical Machine Learning. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Information Technology

Learning outcomes

On completion of the course, the student should be able to:

  • discuss how linear algebra is used when solving problems in data science;
  • explain how the most common matrix factorizations are computed numerically;
  • implement and code numerical algoritms covered in the course;
  • analyze algorithms' computational and memory complexity and discuss efficient implementations;
  • argue for and apply linear algebra tools, such as principal component analysis, to various practical problems in data science.


The four fundamental subspaces associated with a matrix. Matrix factorization (decomposition) as a concept and idea. Least squares solutions to linear systems and applications in regression models. QR factorization, Householder and Givens rotations. Constrained least squares.

Methods for finding eigenvalues and eigenvectors (power method and QR method). Singular value decomposition (SVD) and applications. Principal component analysis and how it can be used for dimension reduction. Matrix-free metods.

Sparse storage format. Tensors and some of its applications in machine learning.


Lectures, problem solving, assignments.


Final exam (4.5hp) and assignments (3hp).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Reading list

Reading list

Applies from: Spring 2022

Some titles may be available electronically through the University library.

  • Strang, Gilbert Linear algebra and learning from data

    Wellesley, MA: Wellesley-Cambridge Press, [2019]

    Find in the library