Programme Outlines and Overviews

Mathematics for Intelligent Systems 7.5 credits

Course content

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
Hypothesis tests
Single factor design experiments

Entry requirements

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, Computer Science or 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: Second cycle

Course/Ladok-code: TMAR21

School: School of Engineering