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Statistikinlärning för doktorander

For you that are a PhD student who wants to understand some of the modern statistical tools that are used to analyze big complex datasets concerning classification, resampling methods, linear model selection with regularization, and unsupervised learning. In the examination is included presentation of the use of statistics in your research alternatively presentation of a related statistical topic.


Antal hp

7,5 hp

Ges av

Fakulteten för teknik

Öppen för

Doctoral students at the Linnaeus University





Nästa kurstillfälle

Spring 2022

Studietakt eller motsvarande


Sista ansökningsdag

February 1, 2022



1MA501 Probability Theory and Statistics 7,5 credits or an equivalent course in mathematics, mathematical statistics, or statistics.


Teaching consists of lectures, presentations, laboratory work, and tutoring, both physical and by distance.

  • Linear regression: simple and multiple linear regression with assessing the accuracy of the coefficients and the model and comparison with K-nearest neighbors
  • Classification: logistic regression, linear discriminant analysis, quadratic discriminant analysis, K-nearest neighbors, naive Bayes
  • Resampling methods: cross-validation, bootstrap
  • Linear model selection and regularization: subset selection, shrinkage methods, dimension reduction methods, considerations in high dimensions
  • Unsupervised learning: Principal component analysis, clustering methods