Introduction to statistical learning, multiple linear regression, classification, resampling methods, model selection, regularization, non-linearity, tree-based methods, cluster analysis.
1. Knowledge. The student has knowledge about the most popular statistical models and methods that are used for prediction in science and technology, with emphasis on regression- og 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.
Statistical learning, multiple linear regression, classification, resampling methods, model selection, regularization, non-linearity, tree-based methods, cluster analysis.
Required prerequisite knowledge
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.
Projectworks and written exam
A - F
A - F
The course consists of two parts. 1) Project work that will count 50 % of the overall grade, 2) a written final exam that will count 50 % of the overall grade.
Both the project work and the exam must be passed in order to obtain an overall grade in the course. Candidates that does not pass the project work, cannot resubmit until the next time the course is lectured.
The project work consists of two parts that are equally weighted. The final grade of the project work is given when all parts have been submitted and the project work as a whole is graded.