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 2024-2025
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
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 |
Project work and written exam, assessed with letter grades.The course has two assessment parts. 1) Project work that will count 20 % of the overall grade, 2) A written final exam that will count 80 % 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 do 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.There is no resit exam in the portofolio/project work.Written exam is with pen and paper
