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Mathematics for Intelligent Systems 6 Credits

Course Contents

The course contains elements from various fields of mathematics and mathematical statistics used when intelligent systems and machine learning are developed, used and analyzed.

The course includes the following elements:
- Vector and matrix calculations, linear maps Rn to Rm, eigenvectors and eigenvalues.
- Partial and total order relations, complexity, Big-O notation
- Partial derivatives, gradients, local convexity and extrema for smooth functions Rn to R
- Basic probability theory, Bayes’ theorem
- Discrete and continuous random variables
- Probability distributions, in particular binomial and normal distribution
- Point and interval estimation


The applicant must hold the minimum of a bachelor’s degree (i.e the equivalent of 180 ECTS credits at an accredited university) with at least 90 credits in computer engineering, electrical engineering (with relevant courses in computer engineering), or equivalent. The bachelor’s degree should comprise a minimum of 15 credits in mathematics. Proof of English proficiency is required.

Level of Education: Master A1N
Course code/Ladok code: TMIR29
The course is conducted at: School of EngineeringLast modified 2019-06-10 13:41:27

Content updated 2020-08-25