Course
Statistical Learning (STA530)
Facts
Course code STA530
Credits (ECTS) 10
Semester tuition start Autumn
Language of instruction English
Number of semesters 1
Exam semester Autumn
Time table View course schedule
Literature The syllabus can be found in Leganto
Introduction
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.
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
Portfolio with two hand inns
Weight 1/5
Marks Letter grades
Exam system Canvas
Written exam
Weight 4/5
Duration 4 Hours
Marks Letter grades
Exam system Paper Based
Project work and written exam, assessed with letter grades.
The course has two assessment parts. 1) Portfolio with 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 portfolio and the exam must be passed in order to obtain an overall grade in the course. The portfolio consists of two parts that are equally weighted. The final grade of the portfolio is given when all parts have been submitted and the portfolio as a whole is graded.
There is no resit exam for the portofolio/project work. Candidates that do not pass the portfolio/project work, can submit next time the course is lectured.
Written exam is with pen and paper. The written exam is in English, but may be answered in either English or Norwegian.
