Machine Learning

Education facts

Code: 4DV660

Covers fundamental concepts in statistical learning and machine learning. It includes topics such as linear regression, classification methods, resampling methods, model selection and regularization, non-linear models, tree-based methods, support vector machines, unsupervised learning, and more. The course provides a comprehensive introduction to the theoretical and practical aspects of statistical learning, with a focus on real-world applications and examples. It also trains ML related R/Python programming skills and exercises to reinforce learning and facilitate practical implementation of the concepts discussed. Overall, it serves as a valuable resource for students, researchers, and practitioners interested in statistical learning and its applications in various fields.

Entry requirements

General entry requirements for second-cycle studies, plus specific entry requirements English 6/ English B

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: 18 January 2027

  End date: 28 March 2027

  Pace of study: 33 %