Entry requirements

60 credits of which 15 credits in mathematics and 30 credits in computer science. Probability and Statistics or Probability and Statistics DV. Algebra and Geometry or Linear Algebra and Geometry I. Participation in a second course in programming. An introduction to scientific computing or numerical methods is recommended.

Learning outcomes

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

• explain and compare basic machine learning methods;
• use machine learning software in practical applications;
• evaluate the applicability of the studied methods;
• analyse the social and ethical consequences of machine learning in specific applications.

Content

This is a practical introduction to machine learning: its terminology, an overview of basic supervised and unsupervised methods (for example, regression, classification, decision trees, clustering), use of established tools for machine learning, application of the methods to real data, and practical aspects such as dimensionality reduction and cross validation. The course also introduces ethical aspects of bias in machine learning.

Instruction

Lectures, laboratory work and assignments.

Assessment

Written exam and oral and written assignments.

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.