Introduction to Machine Learning

Education facts

Code: 2DV516

The course covers machine learning concepts and methods. The following topics are covered in the course: basic statistical concepts supervised and unsupervised learning linear and polynomial regression, logistic regression decision trees Support vector machines unsupervised learning using the k-means clustering algorithm algorithm evaluation using cross-validation and mean square error evaluation metrics such as precision, recall, and F-score algorithm implementation using Python

Entry requirements

60 credits including 7.5 credits of Linear Algebra (e.g 1MA133 or 1MA403 or equivalent) 15 credits of Programming (e.g 1DV501 and 1DV502 or equivalent)

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Introduction to Machine Learning

  Linnaeus University (Kalmar Växjö)

  Start date: 30 March 2026

  End date: 7 June 2026

  Pace of study: 50 %