Statistical Learning (STA530)
Introduction to statistical learning, multiple linear regression, classification, resampling methods, model selection, regularization, non-linearity, tree-based methods, survival analysis, cluster analysis, multivariate methods. Apply the methods in R.
Course description for study year 2025-2026. Please note that changes may occur.
Course code
STA530
Version
1
Credits (ECTS)
10
Semester tution start
Autumn
Number of semesters
1
Exam semester
Autumn
Language of instruction
English
Content
NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by August 20th for the autumn semester.
Statistical learning, multiple linear regression, classification, resampling methods, model selection, regularization, non-linearity, tree-based methods, survival analysis, cluster analysis, multivariable methods. Apply the methods in R.
Learning outcome
1. Knowledge. The student has knowledge about the most popular statistical models and methods that are used for inference and prediction in science and technology, with emphasis on regression and classification models and generalisations of these.
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 the 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
Recommended prerequisites
A basic course in probability and statistics equivalent to STA100 Probability and statistics 1. Basic university level mathematical analysis and linear algebra corresponding to MAT100 and MAT200. Experience with use of software, preferably R. At least one higher level course in statistics like for instance STA500 or STA510 is preferable but not an absolute requirement for taking the course.
Exam
Portfolio and written exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Portfolio with two hand inns | 1/5 | Letter grades | ||
| Written exam | 4/5 | 4 Hours | Letter grades |
Course teacher(s)
Course teacher:
Tore Selland KleppeCourse teacher:
Jimmy Huy TranCourse coordinator:
Bjørn Henrik AuestadMethod of work
Lectures, exercises/datalab, project work.