Machine Learning

Education facts

Code: 2DT916

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 MATLAB

Entry requirements

60 credits including 7.5 credits of Linear Algebra (e.g. 1MA901 or equivalent) 7.5 credits of Appliead probability and statitics (e.g. 1MA915 or equivalent) 7.5 credits of Programming (e.g 1DT901 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.

• Machine Learning

  Linnaeus University (Kalmar Växjö)

  VÄXJÖ

  Start date: 29 March 2027

  End date: 6 June 2027

  Pace of study: 33 %