A good understanding of linear algebra and calculus will be an advantage.
Learning outcomes
On successful completion of the course, the student should be able to
the most important aspects of supervised learning
build, estimate and interpret linear and non-linear models
create and interpret basic models for classification of data
use a programming language to apply machine learning techniques on large data sets
Course contents
Multiple regression. Non-linear transformations of data. Confidence intervals for estimation of parameters. Logistic regression. Support vector machines.
Teaching methods
Lectures, work in small groups and compulsory assignments. Computer lab. If needed, the course is taught in English. The course has an expected workload of around 200 hours.
Examination requirements
Required assignments must be approved, see Canvas for more information.
Assessment methods and criteria
A 5-hour written examination. Graded assessment.
Evaluation
The person responsible for the course, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.