Statistical Learning (STA530)
Introduction to statistical learning, multiple linear regression, classification, resampling methods, model selection, regularization, non-linearity, tree-based methods, cluster analysis.
Course description for study year 2022-2023
Semester tution start
Number of semesters
Language of instruction
1. Knowledge. The student has knowledge about the most popular statistical models and methods that are used for prediction and inference in science and technology, with emphasis on regression and classification type statistical models.
2. Skills. The student knows, based on an existing data set, how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software (R). The student knows how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses.
Required prerequisite knowledge
|Form of assessment||Weight||Duration||Marks||Aid|
|Written exam||1/1||4 Hours||Letter grades||No printed or written materials are allowed. Approved basic calculator allowed|