Upon successful completion of the course, the student will be able to
Understand the most important principles of supervised learning and how to evaluate them
Build, estimate and interpret linear and non-linear regression models.
Build and interpred simple models for classification of data
Use a programming language to apply machine learning techniques for large data sets.
Contents
Statistical methods in machine learning. Multipple regression. Non-linear transformation of data. Confidens intervals in parameter estimation. Logistic regression. Support vector machines.
Teaching and learning methods
Lectures, group work, mandatory hand-ins. Estimated workload of the course is 267 hours.
Examination requirements
Approved mandatory hand-ins. See Canvas for more information.
Examinations
5-hours written exam under supervision. The exam is graded.
Student evaluation
The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.